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Really great article, especially for someone like me who only has a layman's understanding of the biology involved. Thinking out loud: In computer science term

Really great article, especially for someone like me who only has a layman's understanding of the biology involved. Thinking out loud: In computer science terms, the method scientists use to discover and characterize genes (CRISPR, etc) seems akin to studying the assembly of a program, occasionally splicing chunks of assembly into other programs, and observing changes in the output after running. This sounds like a huge slog, and it is amazing that the method works. One interesting thing from the article was that, even after determining the function of CRISPR, scientists still had difficulty in understanding the mechanical/chemical means of its behavior: the gene is a black box, and its expression can only be deduced when observing its effects after running. Have there been attempts to characterize genes on a more basic level, eg by modelling how a given nucleotide sequence encodes a protein, and deducing its function from there? Is our understanding of protein folding still inadequate? Or would reconstructing a protein's physical structure still not give us enough insight into its intended function?

reasonattlm 11y ago
Evolution produces promiscuous reuse of component parts, and everything is linked to everything else: a cell is a bag of interacting feedback loops in solution. At a detail level, researchers still only have a sketch of the high points of cellular biochemistry. Any particular protein may have numerous roles, and scientists continue to uncover new important roles for even very well known proteins, those studied heavily for decades in some cases. Modeling is prevalent and helps. Deducing function with any accuracy requires much better and more comprehensive models of cellular biochemistry, however. Those models lie decades of work from here, meaning that altering genes and watching the outcome in mammalian cells and individuals will be the primary mode of exploration and validation for a while yet.
sjg007 11y ago
Yep. It's basically debugging assembly code and also designing black and white box tests to figure out how the code works.
deleted 11y ago
[deleted]
noname123 11y ago
>This sounds like a huge slog, and it is amazing that the method works. Thanks to programming, a lot of this is automated. In Biology, you can use programmable robotics arm that perform thousands of experiments (high-throughput screening). So for CRISPR, suppose you want to test the thousand genetic variation identified previously computationally for a particular cancer; you can take a cancer cell-line, apply gene-editing targeted at each of those sites each in a slot in a 96 plate-well and observe if the cancer cells' tumor growth. These 96 plate-wells are then fed into image analysis program to quantify the tumor growth. >Is our understanding of protein folding still inadequate? Or would reconstructing a protein's physical structure still not give us enough insight into its intended function? The field that simulate protein-to-protein simulation is called MD (molecular dynamics) simulation. The issue is the simulation is really complex. So you start at the molecular level, whether one protein can attach to another protein's end like Lego's, then you have to account for the individual cellular level, called cell circuits (modeling an individual cell like a logic circuit, different genes producing proteins that regulate one another), then you have to account for the multicellular interactions (how cells influence another). Instead of trying to simulate everything, you have people comparing and studying things at different level.
dekhn 11y ago
FOrward prediction of protein function from its physical structure is still considered impossibly hard if done de novo- IE, assuming no external knowledge. In principle, one could simulate proteins and extract this kind of information, but it's not computationally accessible. Folding the protein is only one part of it. This part, while not "solved", has seen an enormous amount of progress in the past few years, such that we can often predict the coarse-grained 3D structure of a protein, although not the fine details in most cases. Understanding the enzymatic action of proteins typically requires simulating chemistry at the quantum (bonds breaking and forming) level around the active site. Since biologists are more interested in getting results quickly, rather than solving the fundamentally hard problems (the former gets grant money more easily), and because structures that are similar tend to have similar functions, they tend to use structural homology- similarity to a protein of known function- to infer the function of a protein. Although much of my prior work was designed to address the forward prediction problem, I have acknowledged that shortcuts produce more valuable data. And often times, that does involve finding a gene whose protein product behavior is cryptic, and then using structural homology, and other indirect methods, to refine the function of the protein. As for the "computer science terms", I can mention that after working with large distributed systems for a long time, I treat debugging them a lot more like how biologists deal with cryptic proteins than trying to understand them from first principles. I often run "experiments" by injecting things into the distributed systemns, and monitoring them, much like scientists monitor proteins using fluorescence.

reasonattlm 11y ago

Evolution produces promiscuous reuse of component parts, and everything is linked to everything else: a cell is a bag of interacting feedback loops in solution. At a detail level, researchers still only have a sketch of the high points of cellular biochemistry. Any particular protein may have numerous roles, and scientists continue to uncover new important roles for even very well known proteins, those studied heavily for decades in some cases. Modeling is prevalent and helps. Deducing function with any accuracy requires much better and more comprehensive models of cellular biochemistry, however. Those models lie decades of work from here, meaning that altering genes and watching the outcome in mammalian cells and individuals will be the primary mode of exploration and validation for a while yet.

sjg007 11y ago

Yep. It's basically debugging assembly code and also designing black and white box tests to figure out how the code works.

deleted 11y ago

[deleted]

noname123 11y ago

>This sounds like a huge slog, and it is amazing that the method works. Thanks to programming, a lot of this is automated. In Biology, you can use programmable robotics arm that perform thousands of experiments (high-throughput screening). So for CRISPR, suppose you want to test the thousand genetic variation identified previously computationally for a particular cancer; you can take a cancer cell-line, apply gene-editing targeted at each of those sites each in a slot in a 96 plate-well and observe if the cancer cells' tumor growth. These 96 plate-wells are then fed into image analysis program to quantify the tumor growth. >Is our understanding of protein folding still inadequate? Or would reconstructing a protein's physical structure still not give us enough insight into its intended function? The field that simulate protein-to-protein simulation is called MD (molecular dynamics) simulation. The issue is the simulation is really complex. So you start at the molecular level, whether one protein can attach to another protein's end like Lego's, then you have to account for the individual cellular level, called cell circuits (modeling an individual cell like a logic circuit, different genes producing proteins that regulate one another), then you have to account for the multicellular interactions (how cells influence another). Instead of trying to simulate everything, you have people comparing and studying things at different level.

dekhn 11y ago

FOrward prediction of protein function from its physical structure is still considered impossibly hard if done de novo- IE, assuming no external knowledge. In principle, one could simulate proteins and extract this kind of information, but it's not computationally accessible. Folding the protein is only one part of it. This part, while not "solved", has seen an enormous amount of progress in the past few years, such that we can often predict the coarse-grained 3D structure of a protein, although not the fine details in most cases. Understanding the enzymatic action of proteins typically requires simulating chemistry at the quantum (bonds breaking and forming) level around the active site. Since biologists are more interested in getting results quickly, rather than solving the fundamentally hard problems (the former gets grant money more easily), and because structures that are similar tend to have similar functions, they tend to use structural homology- similarity to a protein of known function- to infer the function of a protein. Although much of my prior work was designed to address the forward prediction problem, I have acknowledged that shortcuts produce more valuable data. And often times, that does involve finding a gene whose protein product behavior is cryptic, and then using structural homology, and other indirect methods, to refine the function of the protein. As for the "computer science terms", I can mention that after working with large distributed systems for a long time, I treat debugging them a lot more like how biologists deal with cryptic proteins than trying to understand them from first principles. I often run "experiments" by injecting things into the distributed systemns, and monitoring them, much like scientists monitor proteins using fluorescence.