5 ms·

Cells seem to decode their fate through optimal information processing

153 points by Errorcod3 8y ago

dannykwells 8y ago
I spent some time in the Gregor lab during grad school. I'm obviously biased but I think the work represents some of the most original happening right now in biophysics. These papers were extremely rewarding to read and represent almost a decade of work on part of many members of the lab. For those interested, I recommend diving into some of the lab's earlier work as well as the work of Bill Bialek, Thomas's advisor, who formulated a lot of these theories for photon sensing in the eye decades ago.
- selimthegrim 8y ago
  Have you got any references for Bialek’s theory of photon sensing?
  - d136o 8y ago
    Bialek is an amazing explainer, you'll be left convinced it's all so simple and straightforward. He wrote a book and taught a graduate level class around it, you'll find a draft of the book available at [1] Chapter 1 cover photon counting: ``` 1. Photon counting in vision (Lectures W 8 Feb through W 22 Feb 2012) In this Chapter, we will see that humans (and other animals) can detect the arrival of individual photons at the retina. Tracing through the many steps from photon arrival to perception we will see a sampling of the physics problems posed by biological systems, ranging from the dynamics of single molecules through amplification and adaptation in biochemical reaction networks, coding and computation in neural networks, all the way to learning and cognition. For photon counting some of these problems are solved, but even in this well studied case many problems are open and ripe for new theoretical and experimental work. The problem of photon counting also introduces us to methods and concepts of much broader applicability. We begin by exploring the phenomenology, aiming at the formulation of the key physics problems. By the end of the Chapter I hope to have formulated an approach to the exploration of biological systems more generally, and identified some of the larger questions that will occupy us in Chapters to come. ``` [1] http://www.princeton.edu/~wbialek/PHY562.html http://www.princeton.edu/~wbialek/PHY562.html
    - selimthegrim 8y ago
      Thank you immensely!
entee 8y ago
This is super cool! People underestimate how insanely chaotic a cell is at a molecular level. Often diagrams show blobs cleanly interacting but the reality is more like the images linked here: https://mgl.scripps.edu/people/goodsell/illustration/public/ https://mgl.scripps.edu/people/goodsell/illustration/public/ Everything touches everything. Everything is always moving around. This work suggests that the cell has taken advantage of this enormous challenge. If everything is always in motion, that means you have trouble controlling things, but that gives you a chance to maximally sample your environment. This makes this sort of efficient data processing possible. Downstream are a number of mechanisms that help make sense of that signaling, denoising the chaos. One example from a lab I worked in briefly (old but still cool): https://www.sciencedirect.com/science/article/pii/S0092867411002431 https://www.sciencedirect.com/science/article/pii/S009286741... https://www.ncbi.nlm.nih.gov/pubmed/18599789 https://www.ncbi.nlm.nih.gov/pubmed/18599789
- jmole 8y ago
  Awesome images, thanks for sharing. I wish they'd have put these in science books growing up. Are these to scale? It's interesting to see how much more particular (as in, composed of large particles) these cells look compared to the illustrations I'd seen growing up. I always imagined the proteins that made up a cell wall would be more on the order of how grass looks to a human, in terms of size respective to the cell. Or another analogy, I used to think of a cell like a room (the cell membrane) with furniture in it (the organelles). Now it seems more like just a big pile of furniture.
  - improbable22 8y ago
    Another famous example: The magic of the Golgi stain, which first produced pictures of neurons, was that it stained only a tiny fraction of the cells. That's why you can obtain a picture with cells reaching out and occasionally connecting. Whereas in reality all that empty space is other cells, doing the same thing. More modern pictures are like so: http://www.conte.harvard.edu/news/2015/8/18/imaging-the-brains-jungles-a-miniscule-yet-monumental-leap-forward http://www.conte.harvard.edu/news/2015/8/18/imaging-the-brai...
  - 8y ago
j7ake 8y ago
William Bialek is my favorite scientific speaker (he has some good ones on youtube). His depth in such a wide range of sciences and topics is remarkable. I think Thomas Gregor has some of the most precise biological measurements at the single molecular level. The combination of the theory and precision measurements in studying the fly embryo by these people have resulted in very unique and creative progress in the field. From what I hear when they first started this work, the old-school developmental biologists thought what they were doing was absurd. They have successfully put a much more quantitative perspective back into biology.
dr_dshiv 8y ago
These biological computers are intrinsically based on vibrations. The vibrations aren't just a source of diffusion and Brownian motion; they cohere into meaningful harmonic structures. Alan Turing described morphogenesis in terms of inhibition and excitation loops, which gives rise to banding patterns due to oscilatory harmonics and resonances [1]. We are so accustomed to thinking about things in terms of discrete, separable parts, we have a hard time imagining emergent temporal structures. Living organisms, from cells to brains to cities, are composed of interacting waves and harmonic structures. (I'm emphasizing a hippie-style "resonance and harmony" language here because it really is so critical for understanding these systems. [1] Yang, L., Dolnik, M., Zhabotinsky, A. M., & Epstein, I. R. (2002). Spatial resonances and superposition patterns in a reaction-diffusion model with interacting Turing modes. Physical review letters, 88(20), 208303.
carapace 8y ago
This reminds me of the work over at Levin lab. https://ase.tufts.edu/biology/labs/levin/ https://ase.tufts.edu/biology/labs/levin/
MetaMonk 8y ago
Is there an information equivalent to gravity, e.g. some sort of gradient is formed that the cell simply follows like a bowling ball on a sheet?

153 points by Errorcod3 8y ago

I spent some time in the Gregor lab during grad school. I'm obviously biased but I think the work represents some of the most original happening right now in biophysics. These papers were extremely rewarding to read and represent almost a decade of work on part of many members of the lab. For those interested, I recommend diving into some of the lab's earlier work as well as the work of Bill Bialek, Thomas's advisor, who formulated a lot of these theories for photon sensing in the eye decades ago.

selimthegrim 8y ago
Have you got any references for Bialek’s theory of photon sensing?
- d136o 8y ago
  Bialek is an amazing explainer, you'll be left convinced it's all so simple and straightforward. He wrote a book and taught a graduate level class around it, you'll find a draft of the book available at [1] Chapter 1 cover photon counting: ``` 1. Photon counting in vision (Lectures W 8 Feb through W 22 Feb 2012) In this Chapter, we will see that humans (and other animals) can detect the arrival of individual photons at the retina. Tracing through the many steps from photon arrival to perception we will see a sampling of the physics problems posed by biological systems, ranging from the dynamics of single molecules through amplification and adaptation in biochemical reaction networks, coding and computation in neural networks, all the way to learning and cognition. For photon counting some of these problems are solved, but even in this well studied case many problems are open and ripe for new theoretical and experimental work. The problem of photon counting also introduces us to methods and concepts of much broader applicability. We begin by exploring the phenomenology, aiming at the formulation of the key physics problems. By the end of the Chapter I hope to have formulated an approach to the exploration of biological systems more generally, and identified some of the larger questions that will occupy us in Chapters to come. ``` [1] http://www.princeton.edu/~wbialek/PHY562.html http://www.princeton.edu/~wbialek/PHY562.html
  - selimthegrim 8y ago
    Thank you immensely!

entee 8y ago

This is super cool! People underestimate how insanely chaotic a cell is at a molecular level. Often diagrams show blobs cleanly interacting but the reality is more like the images linked here: https://mgl.scripps.edu/people/goodsell/illustration/public/ https://mgl.scripps.edu/people/goodsell/illustration/public/ Everything touches everything. Everything is always moving around. This work suggests that the cell has taken advantage of this enormous challenge. If everything is always in motion, that means you have trouble controlling things, but that gives you a chance to maximally sample your environment. This makes this sort of efficient data processing possible. Downstream are a number of mechanisms that help make sense of that signaling, denoising the chaos. One example from a lab I worked in briefly (old but still cool): https://www.sciencedirect.com/science/article/pii/S0092867411002431 https://www.sciencedirect.com/science/article/pii/S009286741... https://www.ncbi.nlm.nih.gov/pubmed/18599789 https://www.ncbi.nlm.nih.gov/pubmed/18599789

jmole 8y ago
Awesome images, thanks for sharing. I wish they'd have put these in science books growing up. Are these to scale? It's interesting to see how much more particular (as in, composed of large particles) these cells look compared to the illustrations I'd seen growing up. I always imagined the proteins that made up a cell wall would be more on the order of how grass looks to a human, in terms of size respective to the cell. Or another analogy, I used to think of a cell like a room (the cell membrane) with furniture in it (the organelles). Now it seems more like just a big pile of furniture.
- improbable22 8y ago
  Another famous example: The magic of the Golgi stain, which first produced pictures of neurons, was that it stained only a tiny fraction of the cells. That's why you can obtain a picture with cells reaching out and occasionally connecting. Whereas in reality all that empty space is other cells, doing the same thing. More modern pictures are like so: http://www.conte.harvard.edu/news/2015/8/18/imaging-the-brains-jungles-a-miniscule-yet-monumental-leap-forward http://www.conte.harvard.edu/news/2015/8/18/imaging-the-brai...
- 8y ago

j7ake 8y ago

William Bialek is my favorite scientific speaker (he has some good ones on youtube). His depth in such a wide range of sciences and topics is remarkable. I think Thomas Gregor has some of the most precise biological measurements at the single molecular level. The combination of the theory and precision measurements in studying the fly embryo by these people have resulted in very unique and creative progress in the field. From what I hear when they first started this work, the old-school developmental biologists thought what they were doing was absurd. They have successfully put a much more quantitative perspective back into biology.

dr_dshiv 8y ago

These biological computers are intrinsically based on vibrations. The vibrations aren't just a source of diffusion and Brownian motion; they cohere into meaningful harmonic structures. Alan Turing described morphogenesis in terms of inhibition and excitation loops, which gives rise to banding patterns due to oscilatory harmonics and resonances [1]. We are so accustomed to thinking about things in terms of discrete, separable parts, we have a hard time imagining emergent temporal structures. Living organisms, from cells to brains to cities, are composed of interacting waves and harmonic structures. (I'm emphasizing a hippie-style "resonance and harmony" language here because it really is so critical for understanding these systems. [1] Yang, L., Dolnik, M., Zhabotinsky, A. M., & Epstein, I. R. (2002). Spatial resonances and superposition patterns in a reaction-diffusion model with interacting Turing modes. Physical review letters, 88(20), 208303.

carapace 8y ago

This reminds me of the work over at Levin lab. https://ase.tufts.edu/biology/labs/levin/ https://ase.tufts.edu/biology/labs/levin/

MetaMonk 8y ago

Is there an information equivalent to gravity, e.g. some sort of gradient is formed that the cell simply follows like a bowling ball on a sheet?