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I have been teaching life sciences for nearly a decade now. Still something keeps giving me mental distress each time I think of it. I feel no representations of cell biology can convey information in a truly bona fide fashion.

Think about the following example. If I want to know about tigers, a text description will help.

Many tigers possess stripes on their face, sides, legs and stomach. The striping is varied in width, length, whether they are single or double-looped, coloration from a light brown to dark black and are not symmetrical from one side of the tiger to the other. (source: https://seaworld.org/animals/all-about/tiger/characteristics)

But no matter how useful this text is, it does not give me as much intuition as a painting does. source: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRzxkV7xDm31H6Cve06602uewqWFZkTcCRAQg&usqp=CAU

Still, a much better representation of a tiger is a photograph, as it gives much more direct knowledge about a tiger. Before the invention of full-color photography, the best thing I could get was below.

source: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRWhxfZcx-kgq93mz05__UZ8-gsY0xTFSOkQ19E6mnWObAuJWsdIkwUGMwkFykRyMA536c&usqp=CAU

This black-and-white photo is even worse than the painting, because it removes the essential colors. It is the invention of full-color photography and videos that would eventually give me the most direct knowledge about a tiger.

source: 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

With the analogy of a tiger, I can better describe limitations of information carried by existing forms of life sciences materials. For example, one day I want to learn transcription factors and their control of gene expression. I can find a piece of text on this topic:

Some transcription factors bind to a DNA promoter sequence near the transcription start site and help form the transcription initiation complex. Other transcription factors bind to regulatory sequences, such as enhancer sequences, and can either stimulate or repress transcription of the related gene. (source: https://www.nature.com/scitable/definition/transcription-factor-167/#:~:text=Some%20transcription%20factors%20bind%20to,transcription%20of%20the%20related%20gene.)

But I feel this is not enough to give me an intuition about TF. I then found an image representation of the process.

data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAoHCBYVFRgSFhUZGRgWGhkYGRgWHBUZFhgYGhwZGhoZHB8cIS4lHR4sIRoYJzgmLDAxNTU2GiU7QDszPy40NTEBDAwMEA8QHxISHzorJCs3NjY2OjY6MTQ2NTo0NDQ0Nj09PTc/Nj09NDQ0NDQ2ND8/Nj00NDQ0ND02NzE0MTQ0Pf/AABEIAK4BIgMBIgACEQEDEQH/xAAbAAEAAgMBAQAAAAAAAAAAAAAABAUDBgcCAf/EAEcQAAIBAgMDBwgHBgUDBQAAAAECAAMRBBIhBQYxIkFRYXGBkRMyQlJyobHRBxRigpLB0hUjU1SishYXQ+HwM5PCNGNzo8P/xAAZAQEAAwEBAAAAAAAAAAAAAAAAAQIEAwX/xAApEQEBAQABAwMCBQUAAAAAAAAAAQIRAwQSITFRE0EUUmFxgSMkMpGx/9oADAMBAAIRAxEAPwDs0REBERARE8M4AuSABxJ0Age4lHi97MHT0auhPQmZ/wCwGQf8eYP137cjQNqia/ht8MG+grgH7YdPewAlxhsWlQZkdXHSrBh7oEiIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICYqlQKCzEBQLkkgADpJMi7W2pTw1M1arWUaADVmPMFHOZyfeHeStjHy6ql+RSW5ueYtbzm/4IG17e3/RLphlDtwztcIOwcW7dB2zSq+MxWMfKzVKrH0FByj7q8kds2fd3cJmtUxJKrxFNTyj7R9HsGvWJ0DB4GnRXJTRUHQoAv1npPWYHL8DuDinF3yUx0O128EuPfLH/LZ7f+oS/sNb+6dJiByrE/R7iV1R6b9QLKfeLe+UuI2Li8OczUqiW9NLkD7yEgeM7fEDjmz99MXS0NTygHo1AG/qFm982zZn0h0XsKyNTPrDlp7uUPAzYNo7vYavfylFST6QGVvxLYnvmpbT+jni2Hq/dq/qUflA3jBY+lWGanUVx9kg27RxHfJc4jjdjYrCtnZHS3B0Jy/jQ6d9pc7B3xxWYU2Zai8+ccoAc+Yce+8rrUzLq+0TnN1Zme9dViUWA3kpPo3IP2vNPYfnaXYN9RK46mdznN5W309YvGpw9xExu4ALEgAC5J0AA4kzooyRI2ExlOqCabq4BsShDAHjY2mVHDAMCCDqCNQR1QMkREBESv2vtalhqZqVWsOYCxZj0KOcwLCfCZyja2/uJqEilainNazOR1sRYdw75Rj61iTf9/Wv/wDI6/IQO01toUU86rTX2nUfEyDV3nwa8cTT+62b+285dQ3PxjcMOw9oovxa8n09wMYeIpr7Tn/xUwN2qb8YEf6xPspV/TItT6QcIOAqN2IB/cwmvUvo5xB86rSHs52+KiS6f0an0sSPu0/m8CZU+kih6NCqe3yY+DGXW7O8S40OVQp5MqNSCTmB6BpwlFT+jal6VeofZVV+N5sO727lPB5/Js7Z8t85U+be1rAdMC7iIgIiICIiAkDa206eHpNWqGyrwHOzcyjpJkuo4UFiQAASSdAAOJM45vXt1sZW5N/JqctNdbtfTNbpb4WEDBtTaNbHVwbEsxy06a6hR0D4k9XROibqbqJhVFR7PWI1biqX9FP1c/VG5m7QwyeUcXrONfsKfQHX0n5TaoCIiAiIgIiICIiB5lRit3cO5LeTVWPFkAUnttoe+W8SNZmpxU51c3mX1aXj92aiXNMh16ODD8j/AM0lbhcdWoNlVmW3FWBt3g8J0eRcVgkqCzqD28R2HiJh32cl8uneK3Y762ePVnlFFgt6lOlVCp9ZdV7xxHvlrWqJiKNRKbqc6Ml78Cykajjzyoxu6g1NJ7fZfUfiGo8DNXOZW5IJsdGS9j1qejrjO+6zeNZ5/ZOun2u5znXj+7bl2PVYLmbLkRwtq1apyyaRR7sFtlyPpr53bI2K3dreTNOlVCXRR5zjLUC1Aai6H1k0FvMFitpU0NvV09JiPtrcd54++WNPeyp6VNT2Er852vdTPpuWX9Y4ztN69cWWfpUptg1QSwqZsxuys9ZVYlq5DXU3BUVKdrfwgLiykfcPsOqKoqPWLKtU1CMzctbVsoIAFspqJoS2lMdQEGpvbUNwtIX6bO4HgBrKvGbWrVdHL2NhlCMq68Bw175Ou4vj5Zzb/F4Rntub461J/M5bRtTeNEulOzv0+ivaec9QmqVKFTFMVYFy3G/BR0/ZAlps3dt3s1TkL0eke7m7/Cbbg8GlJciKAPeT1nnMzZx1uvqa36Rp11Oh0M3OJ5X7qDYO5lDD8pwKr3uC4BVRzBVOl/tceybQBbQT7Ps9F5pERAREQEREBERAREQEREDSPpH2z5OmMKp5VUXe3EUweH3jp2Ayo+jrYYdzi3HJpnKgPAvzt92/ieqUO82LbEYyoRrd/JoOpTkUd51751/ZOBWhRSivBFA7TxY95ue+BNiIgIiICIiAiIgIiICIiAiIgY6vmt2H4TnVDzF9lfgJ0Wr5rdh+E5zRPJX2R8BNPbfdy6v2ZAgYhSLgsoI6QWAM2x926B9EjsZvnNVp+cntp/cJ0KU7vp51ZzJVu36m8y8Wxz6vQVHdF81XYC+p4zE/AyRtFgK1XUee3OOfUe4yO+osOfQdZOgE1Y4mJJ8OOubq2tr25vPh8I9OnWZg1U2QKjv2k5QfDj0AzEu+WFPA1z2YXGn/APKXr0w1rgGxBFwDYjgR1zLPPaVDh95qTsqLSxV2IUFsLilQXNrszUwFHSToJ92xto0atJAMykhqpCu2RGYIjErogzEtmbTLSfn1F7MTUwb3AOYZTcDUa6HpGp06zA16pvIbplpjLUsRd1L2Z6SpyQeST5Q3B4W57ET0+9aWVgjFXcJe4uCWpo1wL2yu+Q3sLowvewN4MIg4IvnZuA848W4ceuGwqEglFJUkgkAkEm5I00JOsDXcHvSWp03ajynWmMqstvKVEpvlF9Qtqg5R6LdF7bZe1RWepTylWpEAgkG4JZQw6QSjai40te4IEsYRAbhFBta4UXsDcDhwvr2z3ToqpJVQCxuxAALHpNuJgZoiICIiAiIgIiIHEd1qWfG0Adf3gY/du/5Tt04ruY9sdQv6xHirCdqgIiUT7adK1SnUpKETydmR3d2NVmWmMnkxYkqb8rS448YF7EpKm8uHUuCzXp3zgKxKqAzFiBwACMdddOGovlXblIlgudirshCozNmXMW0AvoFOnHhpyluFtEozvFSDZbNz2slQscvlsxtl80Ci9jfW2g1XN5pbyUze6sAM9gAWYrTeqjPYC2W1Im976gWva4X0SpXblNlqsmYmkrtYqVDBGdDlJGozU2F+w8CLx8PvAuRGrBUNRwi5TUdbkqBcuiEG7AWAPTwBsF9EpDvDTBQZamWopZWKsA3KpKgAOpzGqtu+9p9beGiBe7WzBL5WtnIJyXPpCxuON9OJAgXUhohZnuzWVgAAbC2VTza8SeeTJHw/nVPbH9iQPn1Nel/+5U/VMeKxGQBVFyRpe9gBzsePz8SJkq8ZpUN/SRbdeUtcd2YH70irZnN4RK5ZvPYt1Xsv4Rp43kJ6KD0F/Cvyk2pItSc7WzEk9kOlhbuCgCsvKzgDk817c56B2y0qqT5zF/a4fhHJHhMOz2GZ15zZh1gaHwP90kVJPN4RZPL2RHQdA8BIRpKrB1UKw4MoAYd8nVJEqytvDtnMvpVngNtMSKbAFj5pJyg21sbA8rsFjY8OexXaAGjjKea3KDHoXQEnqt8DNVw6FqiAccynsCkMT4D3zY6K5nZvU5K9pALHwKjx6ZE3ph7nMxvjKR9fI1ZCB1EMw7QPyJkxHBAINwdQRwlfUn3Zr2LJzaMvVe4YDvF/vS2d3nis8168VZxETquREQEREBERAREQEREDhlE/V8WL6eRrC/Yj6+4TuU5J9Iez/J4ouByayhx0ZhyXHuB+9N/3P2l9YwtN73ZR5NvaSw94se+BeyK+CRmLlQWJQk9aEsngWJ75KiBVLsPDjMBTHKUoblzyCGXKLnRbOwsNBeen2JQYsxpgluJu1yOXyb3uE/ePyPN5baayziBUYnYNB2VipGQnRSwzArUXK1jfL+9c26+jSZKmxaBvemNSSbFhxLEjQ6KS73XgcxvLOIEJdnUgGAQWdWVuOquzOw72Zj3z5idm0nKFluUFl1YWF1axsdRdFOvOok6IFV+w6FgPJjk+bq9182wU3uoBRSAPNKgi1p7Ox6JU08gyE3IuwzErkOax5QI0IPHW95ZRA+TBh/Oqe2P7EkiQlqZWe6tZmBBALAjIo9G5GoPNAmzBiaKsLNwGt+BBHODzTz9cXof8FX9MwbXP7ojmYop9lmVWHgTE9bwKGvVdicrWTmbKM7Dp1uB4a9XCQnNS4VXJLG1nCFbDUk2AOg6+JA55Zukjolqq350dR23RreCse6d5nPHs6eVn3YvqoU5rnOPTJ5fdzAdQ06pkbHOLKVDE6Cxy95Gth0keHNJTpI9JP3hvzJp3tyvgkXObPWE3Z7MFSmx1Z27FJRR2W18SZGdXXgxI5w2unUdDftNuzjLV6cjVKct4Zs4sWm9S8yrXZ2GRUDoc2cXzniR0W9Gx5urXWSMH5p9up7nYD3ASFsS+RxzCo1u9VY/1FpMwzWLp0HMOxh+oNMG8+OrGbVt1bXupPOD/AOqvsP8AFJ6qRgFu5bmVcvexDEeAU/elc/5RWe60iImh1IiICIiAiIgIiICIiBrW/Ox/rGHJUXqUruvSRblKO0DxAmi7kbdGGrZHNqVWyt0K3ov2a2PUeqdfnLN+92TRY4qkv7pjd1HoOef2SfAnsgdSn2cz3O3y8mFw+JJyDRKh1yjmV/s9B5uzh0hKgYBgQQdQQQQR0giBkiIgIiICIiAiIgIiICYa9EOrIeDAg246846DM0QNcqgoctTQ8A3BH6weAP2Tr2jUxcSUtYtY3uuU3e44FQLknqsZsFepmuvo8D1/7TDTpKvmqB2AD4Sl7qZ9JOXSYvHqq6Yq5QXpEk+oUvbpZWYZT1Amefq1RmDqmUrfz2XlKeKjJm42GptYgceEuYnL8Vv4i3hFO9dRoxyHoeynxOh7QSJjAL6IMx9b0B1luHcLn4y9iX/FXj0nqjw/VCw+GakoROWupOY2bMTdiDa1iSTbmvobWA++Qct5S6qVBAXiCDa4c26hw4cdeEmxM93q+9R9Oc8obM50CWPSzLk/pJY+A7pNwdkAXUHiSbak8Tcad0+T7aTndl5J05EyeKlVVF2YAdJIA98wU6ltDw+H+0g7y7Bp47DthqrMqMVJKEBuSwYcQRzTVnU1OYpZwsRi6frp+JfnPX1hfWXxE5s30LYH+NiB96l+ief8lMF/HxPjS/RLIdN8qvrDxE+5x0jxE5h/kng/5jEeNL9E+f5J4T+YxH/1fpgdQzjpHjIW2dqphqD4mpcpTGZgti1rgGwJF+M52PoWww4YquP+38pA259EQp4eo9HEV6tQLyKZyWdrgWPVA6dsTb2HxieUw9VXHOAbMvUynVT2iWs47uT9FNai64nEYhqTLqKeHYh7aGzOOHWBftnYB0QPUREBMdRAwKkAgggg6gg8QR0TJEDm+8m4bAmrhdRxNI8R7BPEdR9/Caxs7bOJwbFVZkseVTqA5b9atwPWLTt8hY/ZtGsMtWmrjmzAEjsPEd0DRcH9JDiwq4cHpZGK/wBLA/GW9D6QcK3nConaoI/pJn3F/R/hX1U1KfUrAr4MCffKrEfRsfQxPc6fmG/KBslLfHBN/rge0rr8RJNPeTCHhiaXe6j4maHV+jvEjzXpHvYf+MivuHjB6CHscfnaB05Nr4c8MRSPY6fOZRtCkeFVPxL85yZ9ysaP9G/Y9P8AVMR3Oxv8ufxJ+qB2AYun66fiX5z79bT11/EvznHP8I4z+WbxT9U+f4Rxn8s3in6oHYzjKf8AET8S/OeDtGiP9Wn+NfnOQDdDGfyzeKfqnobm43+XP4k/VA60218OONekO10+cj1dvYYCwxFG5FgBUQknmAseM5iNysb/AAP66f6pmobkY0MpNNRYg6unMb8xgdD/AGhSHprPh2pS9cdwb5SobYdUcQov0n3aT0uwqnOyDvPykXt+hPS6Pq9S+0WR2xSHpE9itMVTbtJQWOewBJsvMNemRV2A/O6+BM9Hd64INTiLaL098fT7Wfenl1b9mIb4UOBDobBrPlQlSCQwueFg3ZYzw++NEaBWJ6AGJ1JABAU2JIOhiluZRVBTDEKFZSEVUzBlyMTbnI55mpbpUQSxaozEqSzFbsVN1JsoGnDhwj+2h/VQ330Tk5aZYMVANzlIJUFgSoBtnW/+xmPDb5+UZVSn5wZrngFUIb+drfOug143taWdPdLDL6LGwAALNYWykWHMbohuOOUSTh93cMnm0wOI0J4HLp2cldPsjojy7efY46nyrzt2p0J4H5zydtVelR2KPzl2uyqI9Ad5Y/nMi7PpD/TTwEt9boT2yr4dT5a4drVT6fuX5TN+163r+5flNhTBoTYIoHOQo8O2Svqy+qvgJl7jF6tl6d8Z/wBVvT18tU/bFb1/cvykHaG8WIRlRairmV2zMqtcpksijMoucxPHgp7RvP1dPVXwE+PhKbCxRCOgqpHwnLPbdSX13VZjXy55T32qNYC5v5NQdFBZ/JW4rov71dbk6HThfPhd7aruE5Q5idCAwXOQOTYi2l73vzc83v6ml75FvprYX01HN0x9Tp3vkW9rXyi9ujhLXttfmv8Aup8L8tW/bFb1/cvyj9sVvX9y/KbZ9XX1V8BH1dfVXwEp+F6v56j6evlqf7Xrev7l+U22kbqD0gTz9XX1V8BM079Hpaxz5XlfObPe8vsRE0LkREBERAREQEREBERAREQEREBERA8MAdJgegeY9x+ckxK6zNe6ZbPZBII4gj4eI0n0G8mzG1IHiAe4Tlej8Vfz+WCJm8gOvxPzmFDTZmQNdky5gGa65rlb66XsZH0aeUIJtxmbyA6/E/OfVpAcAB3CTOlTzjANeAJ+HidJ7FEnie4fOSLRL56cnurdV8C20E9RE6KkREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQE1XYuzkTaOOqjNmdcPmJZiDdW5ibaZQB0DQcZtUq8HgWTEYisSMtUUgoHEZFYG/iIFpERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQKvbdV1RfJkhmdFuuTNZm1tnBUadImvUdt4oBiwJARARkvUp1HrV0XNlFmKhEV8oy3IYWW5m6xA1nD7Zqt9YLKQCj1cOxUWZUzLYWJzaCm+tj+9I9GeV2niKIHlBmDi6eVKCoSoW9MeSXKWYtyV48lr9W0RA1jC7drEBPJhntTJY3VStR0RG0Gmpq6f8AtHpE8NvJVUJmoC9QKws1lswQindwt6hzGwHHKdJsiUlDM4UAvbMec20F+6ZoGr1t43UoppKWZS4UMw5J8pl1K9NMXsDbML20zfcHvI1SpTp+SFnAuwZcrEu6EJc8vKEDNlzCzDWbPECkwuNqNi6lI/8ATXPl06EwjDXn1qVP+CXcRAREQEREBERAREQERED/2Q==

This abstract image is a complement to the text. But I don't feel it insightful enough. The next helpful thing I found was a video https://www.youtube.com/watch?v=MkUgkDLp2iE. And the searching process stopped there.

Does current technology allow for truly bona fide representation of cell biology in the same sense as a tiger is represented by a full-color photograph? For students who have strong imaginations, they don't find learning cell biology too much of a trouble. For those that lack imagination, I must strive to find something that gives them as much of an intuition as possible.

The ideal tool to represent cell biology is something that presents the products of imagination for the strong minds in a directly accessible manner. I'm afraid this goes far beyond current technologies. Is it so?

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  • $\begingroup$ Are you ever going to find better things than animations of molecular machinery? Because your tiger analogy only works because you can perceive a tiger with your senses. I also argue that an image of a tiger only seems more bona fide because it is heavily augmented by your real world experience with similar things. Seems very similar to math equations versus graphs. The graphs don't embody the existence of what is there. Even animations for higher level stuff don't make sense unless you already know what you're looking at, if they can even be animated at all. $\endgroup$
    – DKNguyen
    Feb 21 at 16:13
  • $\begingroup$ Or are you just asking if electron microscopic motion cameras exist that work on living samples? $\endgroup$
    – DKNguyen
    Feb 21 at 16:14
  • $\begingroup$ Hi @DKNguyen, thanks for bringing up electron microscopic motion cameras. I did a bit research on my own and found this type of technology can allow up to 1600 fps. That's a fantastic tool for my teaching purpose. And it is vastly better than the abstract images or animations for cells. $\endgroup$ Feb 22 at 2:15
  • $\begingroup$ I think they don't work on living samples though. But maybe still life photos are better for you than generated animations. $\endgroup$
    – DKNguyen
    Feb 22 at 2:32

1 Answer 1

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Does current technology allow for truly bona fide representation of cell biology in the same sense as a tiger is represented by a full-color photograph?

There is no way, that any cell-biological depictions will ever reach the level of intuitive understanding, that is conveyed by such a picture of a tiger.

Next to the tigers image, you see every-day objects like trees and grass, that give immediate sense of the scale. Also, the tiger is a mammal, just as you are, and you have multiple close references at your disposal, like other mammals or cats, which you can immediately extrapolate to gain insights about the tiger. Besides obvious aspects, like the knowledge of anatomical structures, that one effortlessly recognizes, e.g. the tigers eyes, and what seeing or breathing means, you also have a detailed model of the physics of objects at your scale of mass.

On the contrary, being confronted with 2d depictions of molecules, most people would find it hard to guess how the missing information in the 3rd dimension would look like. Also, they lack an intuition of the physics at these small scales.

You would need to be a biological molecule yourself and spend all your 'life' at these scales to understand and feel what it means to be a bio-molecule, and your "molecule brain" must have evolved with pressure to recognize predatory tiger-molecules at first sight, which would otherwise mean your death; only then would you have any chance to come close to achieving the intuitive understanding of such a magnitude.

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