DevoWorm Lab Meetings, 23 Jan 2023

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From YouTube: DevoWorm (2023, #3): 3-D CNNs, recap of Symmetries paper and MEMoDevo, and Tensegrity Networks

Description

A presentation on 3-D CNNs and QCANet (segmentation for GNNs). A short discussion of MeMoDevo symposium and accepted paper in special issue of Royal Society Interface Focus on Symmetries in Biological Systems. Presentation on Biological Tensegrity Networks, with focus on static and dynamic mechanics. Attendees: Richard Gordon, Susan Crawford-Young, Sushmanth Reddy Mereddy, and Bradly Alicea

A

Hi Bradley, hello,.

B

A

All right, fine, everybody! How are you.

B

All right pretty well.

A

I have been saying a lot of research paper related GCL I got some pretty clear idea on what is that about our graph and all I found some new methods also to implement and I wanted to show. You I was very excited about this meeting since two days. Oh.

B

Yeah yeah, that would be great yeah. You told me you had some things you wanted to show yeah.

A

Yeah actually I was reading couple of papers related to segmentation. I found out that devoland was good for segmenting raw data, but there is an issue with it. Actually, when the cells were like very so many cells, it won't segment properly. For that there is a bus method, called qcnet house, I wrote some slides. I will show it to you. Okay,.

B

For season, okay, yeah I look forward to it, so we can. We can you can share your screen in a minute. I just wanted to make sure that we had people coming in it's like.

B

So how are you Susan.

C

I'm fine, you can hear me: okay, yeah.

B

C

Oh okay, yeah I have um I did um a PowerPoint that I could present as well, but um doesn't have to be today. So, okay, just it's just about um cell cytoskeleton. So, okay.

B

That sounds great yeah.

A

Yeah, you can see the screen right, yeah.

B

A

This is actually like a paper called 3D conventional neural networks, basic segmentation. They are using some raw, segmented, fluorescence images for segmenting data. Actually, it is done for mainly for most embryo Genesis, like it is used for most cells, but they have implemented for it for like this also for classical games. Also, after that, they thought, after that, by comparing results with other models like CNN approach, convolutional neural segmentation and unit QC, and it was showing better uh results compared to these are two of them about this is qcnet like it's.

A

A robust, accurate algorithm to a square 3D position of cell would help to Revel mechanism mechanism of embryogenesis, like we need bio, 3D, fluorescence, bio images. uh Actually, this algorithm is completely based on image: processing, algorithms. They fail to I mean some of the CNN fail to detect an image when the object doesn't fit into the pattern that algorithm can process. So algorithm was mainly based on heuristic image, processing, I didn't know the meaning of this I tried to search, but I forgot. Sorry. What.

B

Is it what were you asking.

A

About the fluorescence images, oh.

B

Yeah I don't know what.

A

They are I, asked mayuk actually about this thing uh like how could what is fluorescence images? He told like he never analyzed the data, but epic data set was completely fluores and she told I even uh Hari Krishna last year student was there. No Bradley I talked with him. Also about this thing like what is foreign.

A

Can you just tell me like what is fluorescence images sure.

B

So it's a type of image that they use ultraviolet light usually, and they use a marker that has a fluorescence in it. So it could be like an antibody stain that has like a fluorescence dye in it, where it could be like a something that has a fluorescence marker. The usual use fluorescent elements from other animals that they put into a cell, and you can.

A

D

B

Have put it under uh UV light, you can see these fluorescent markers and these only give some point. Okay of reference.

A

Before presenting that I asked Mike that does Epic at our set was completely fluorescent. Yeah, then only I thought of proceeding to this paper. I'm sharing this paper also in the chat box.

B

So a lot of times, if.

A

B

A black image with some bright dots and you know bright dots throughout that's a fluorescence image.

A

Okay, okay, I'm yeah to implementation. Whatever the paper is completely based on. It has two image: pre-processing things like MSN and ndn, and nuclear segmentation. Nuclear segmentation record just segments the nucleus inside it and uh the Indian segments are outside the perimeter of that after post processing, we will get a 3D segmentation like this so and these are implements like these are the photos from paper. Only these are implements for C elegans like ground truth. Actually they have trained the model, they have compared it with ground truth.

A

We don't know ground truth, but they use it. Some easy open source platform to get the ground truths of them and they have like implemented QC and it then they compare them. They have trained the model.

A

The qcmf was able to solve problems of nucleus Fusion in test data, which is not solved by 3D unit uh and that of missing nucleus detection, which was not solved by 3D Mass RCL, like CNN implementation. Also, uh you see and it was robustly performing instant segmentation for images acquired for different Imaging conditions. Here, I mentioned, like using Fiji open source platform.

A

They have found the ground uh truth of fluorescence images at 11 time points, and this is QC, and it consists of this of what I told the NSN and ndn and the model architecture like foreign.

A

Optimization, it is completely coding part which we can handle and Indian. This is part of Indian, actually I hope. Once you read through the paper, you will get a pretty clear idea. I mean it is good, but it is better than sorry. It is better than the volan when the cell nucleus are very large, like not large, so many cell nuclear sub garments, it really segments well when it is Force, four cells are there. It's quite good.

A

Development is performing well, but when these embryo, when the cells are increasing up till 35 around that this wasn't performing well whatever the whole and platform is, it was completely built on resurrect 18 actually and these effects on demographic also, actually, because demography is using devil and pipeline to get the segmentation of cell nucleus is right. Centroids of that, so maybe we can Implement. That I was like halfway. There I'm editing some code Parts there. Maybe I will show my this thing.

A

Even he said like okay, you can just pass it on get some raw segment data. If it's possible, we can integrate into development. This qcl at implementation, yeah.

B

Yes, that's good, so maybe.

A

Maybe it will be used in demograph. Also I talked with jiangle asked him. Can we use segment raw data? Then he told like he has not. He want to just develop demograph perfectly, because segmentation works well with the development, but actually segmentation doesn't working. Well, then there are so many cells, okay.

A

So that's the reason and I was going through. Some I have made my list. I will probably stay here by tomorrow about uh all graph neural networks. I was seeing some Stanford classes regarding gnm and I have seen through this paper. Also like microscopy videos, implementation, graph, neural network, which was based to demograph yeah. It is quite good, but we need some things to get cleared. I I asked it like jangli. Can we have a meat or something like that? You haven't responded. He was kind of busy, then I asked him.

A

Mike was be so busy. Actually he he mentioned about you, okay. He actually released a paper two days recently. It was the fifth best research paper in the uh Dar dot AI com Community. Even it was competing with Google some of the research papers. It was like weeklies best paper ever released. I asked him like how do you get this off while I was working with Bradley I learned, so many things those I have implemented here? He was telling like that. That's.

B

Great yeah actually.

A

Yeah yeah he the paper, was like big blast. It was completely uh some lawmaker to charge GPT. Actually we know 10gbt works fine, but it is giving correct predictions or not. It is not like defined, so my have completely worked on it. He cleared some like code type of thing, with judges like charge, GPT what it was seeing in my image or in a text what it is focusing on. It is completely about neuron, neural network interpretability, Transformers Etc. Oh.

B

Wow, okay, yeah I, think I have a bookmark and I have to go. Read it yeah in depth. So, okay, okay and.

A

I have seen another one, also lineage population method. There is a framework called history. Actually I think you know this one also Bradley I haven't checked it completely, but I'm sharing it here in the chat.

B

A

Is for a model implementation of lineage population? This is a tool. Actually, uh there is a repo and code. Also there. There is an open source platform to extract these lineage populations, often with his parents or daughters, are like that right now, I didn't got any clear idea about gnl, but this week I will clear totally analyze it. If it's possible I will next week, bye I can show some test output regarding tnm. This was the best thing we can Implement for segmenting broad data actually yeah for the first stage.

A

One stage, one for these methods.

B

Yeah, so the things you posted in the chat, you posted the thing on East tree and then that's the one that you just.

A

A

B

3D convolutional neural networks.

B

Yeah, um so the nuclear segmentation network is this like I, guess: I, don't understand how what they're doing could you bring.

A

That side effects and they're actually segmenting whatever data. We have a lot of sense images up there right into 3D structure from those 3D structure. We can extract these centroids, actually centroids of every cell, which is used in like which can be used in demographic stage. 1 pipeline actually demographs have demographers has two two stages: one was to extract centroids of each cell when we segment it after that we extract lineage population model, so that's like, which is the parents and what was happening.

A

But by this platform we can get best accurate, algorithm better than develop to get segmented raw data and centroids. Actually, by this lineage population open source Network, we can clearly get a CSV file to pass it to stage two to get graph neural network.

B

A

B

A 3D structure first and.

A

Then it's like yeah, okay, yeah!

A

That's it like I've gone through stage one this week like what are the best methods we can Implement to get back to it, uh algorithm for a demographer I found. These two are the better methods for like till now, we need to just integrate them like which fits in demograph pipeline like we need to do a lot of work around it to fit into demograph Pipeline and can I ask one question like what is like future plan of this demo.

A

I mean Devo line or demograph like what do you want to make like any 3D structure? You want to make 4D structure which depends on time cell, dividing like that or like do you want to just create a graph neural network wow.

B

The idea is to have this thing that you can Implement on a data set. You can take, uh you can take out like a set of centroids, you can build a network and then you know so it has like two paths you can use one to like uh extract centroids from some data and then use those for some sort of analysis or two. You could build a network or graph embedding and then use that for different things like so.

B

We have okay, uh you know we have these two pads and this is where Devo graph comes in, because that's the other path. Okay, the first path is mainly like just taking a data set segmenting the cells having some centroids and definitely we're interested in the 4D perspective. So you know the 3D perspective. Of course you have to have that in place, but the time over time we want to see, because you know a lot of the data's. Well, it's essential to have things in time. Yeah.

D

And we've tried to make 40 graphs on 2D pieces yeah.

A

Yeah I have seen this thing like we want, like your idea, was the same thing right I. Have this thing in my mind that we know we need to create a 4D structure which changes according to the time like when the embryogenesis happens, this cell divides right at that time. We need to create some type of thing. We can accurate greatly see where this point, like cell division, was happening. You just want to track the cells out. This is the main area right, yeah.

D

Okay, you can uh I, don't know if it's generalizes to uh four dimensions, but an old trick is to make for three dimensions is to make two views at about six degrees apart in three dimensions and then view them with uh each through a different eye.

A

D

See it in three dimensions, even though it's two two-dimensional images.

A

Actually, can we use depth maps for this? Do you know this thing called depth map in deep learning? They generally make a 2D image to a 4D structure. According to the time we can implement this thing on like uh image analysis for this, uh whatever we want to create depth, maps actually can be used. For this thing, we can use graph URL problems.

D

That's a change in in the brightness right.

A

Yeah yeah yeah well.

D

A

Know, like actually I know like we can change from 2D structure to 3D structure by using depth maps we can like estimate some by using maths. How can we create each cell is changing? We can use that maps for that.

D

Let me give you another hint, maybe if you made time a color, okay, okay, so if you do the sliding you know sliding color scale.

D

Okay, maybe add it to a depth map, but uh that might be a way to represent it.

D

A

A

Actually, uh I've seen why what paper he was implementing the paper was pretty good. There are some loopholes in it. I will mention in next week. I need to read it completely, get a better view of that paper. We can make it more, but I am not getting. What is this topological data analysis actually telling? Whatever will be there? I generally go to YouTube I watch the video and go for an implementation, but whatever the DN part is that there is only one best videos for that. That is Stanford lectures about GNN.

B

Right, right and.

A

About topological data analysis, there is no video like there is no good information they are providing. Even we need to read some book to get a clear idea on that.

B

D

Let me give you another hint: there's any three-dimensional structure and you watch a movie of it rotating. It looks three-dimensional your brain can do that yeah, but required two pictures, just the rotating object. So you have four dimensions. You could have a rotating object. That is also growing.

A

According to the time, the fourth dimension will be time either because.

D

We time and you make a movie, so the movie becomes the time. Okay,.

A

Time lapse: video like a Time, Lapse video. It will differentiate it so.

D

You keep it rotating to get the third dimension, but then you make a movie to get the First Dimension.

D

It's like taking a movie of a growing tree, yeah, okay,.

A

Time lapse radioactive.

D

Rotating tree growing, if the tree is rotating while you're doing the time lapse, you could then see it in the First Dimension.

A

A

um Bradley, can you just help me out with this topological data and it's actually, you should have the one video in a channel demographical data analysis yeah, you have any other resources. Please share to me.

B

Okay, yeah I will I have some, but I have to go, dig them up. I, don't have anything right now, but yeah I mean there's some I I, don't know if people are doing like videos like graph neural networks is a pretty high uh bar of sort of understanding which is hard to get. You know, but that's something that um so I know that geohang worked on this last summer and he came up with the site. You know this paper that he liked and he implemented it and refactored it.

B

And then you know I, don't know what the next step is. There probably I mean definitely needs Improvement, but it's you know it's like uh they're. um You know probably better ways to do it. I guess.

B

Neural network, but.

A

That's something that.

B

We can talk about later.

A

B

B

Yeah well yeah, thanks for that. That was good.

A

Okay, I mean we can improve the stage one process actually double graph. By using these two methods. Yeah, we need to see stage. Two also I will give you an update within a week like I'm, like attention of that, like what will be the best method I will I was I was thinking to have a meeting, but yeah I think he was busy might be so, and now these things comes, I will.

B

Thanks now, I'd like to give a quick update on a few things that we've talked about in previous meetings and I want to go over. So first of all, some news on a manuscript that we've been working on and then secondly, we're revisiting a conference that was attended over the holidays.

B

So the first thing is: is good news on the front of manuscript acceptance, uh our submission to the Royal Society interface Focus was accepted, so we have um see if they have the featured submissions page here. This is the page for the special issue that we're contributing to, and this is our paper embodied cognitive morphogenesis as a road to intelligent systems.

B

This is myself Richard, Gordon and Jesse parent, and this paper was accepted uh last week, so this will be published in interface, focus and a special issue on um making and breaking symmetries in mind and life. So this is the Royal Society interface Focus. This is the journal. It focuses on special issues on different interdisciplinary topics, so I look forward to that being published, and it's now it's available right now on the psy archive um and the abstract is here, but we're looking forward to getting a formally published study very soon.

B

The second thing is uh following up on the memo: Devo Symposium and this was held in Paris uh over the I think it was last month uh in December and I. Remember, I went to this I attended this conference for the group um this was put on by the pasture institute. There's actually been. It was put on by a group of people in Europe who work on um different aspects of mathematical, biology, mathematical Neuroscience. So this was the memo Devo Symposium mechanics, morphogenesis development and evolution.

B

So there were a host of topics we covered in one of our previous meetings. This is a a network of the attendees, the attendees of different interests, and, if you zoom in on one of these nodes, you see that they have different interest areas denoted by the tags, and so you can see that there there's this uh bipartite Network here of different uh attendees and their interests. So I'm trying to find Diva warm I, don't I, don't see it right here, but you know we we're kind of in the middle of the the two groups.

B

I, don't know where. Oh here we are Diva worm group, so this was registered as Diva worm. Group uh here are the tags, interest tags and you can see we're kind of on the edge of this network, uh not quite connecting to this network. This network is filled with people interested in comparative biology, machine learning, microbiology.

B

This group is uh I, think my Neuroscience oriented self-organization information, Theory, Network, neuroscience and so forth, so that that's kind of an interesting way to visualize the attendees and their interests. This is a blog post on the Node, which is run by the company of biologists, which is a developmental biology uh blog. This is hosted by the Journal development and I was going to write a blog post on this conference and some of the things that um I saw that were interesting.

B

But someone else already did this so I'm going to defer to them for right now and go over their blog post and I may publish a a bit about some of the things that I found interesting at that conference later. But for now we'll just go through this post. So this is by Romaine levere who uh attended the conference in person. There wasn't actually an in-person part of the conference in Paris and then a virtual part of the conference online.

B

uh So this title is uh to conference or to unconference a little report of The Institute passed your Q bio Symposium, memo Devo and that's mechanics, morphogenesis development and evolution and it's experimental format.

B

So um this is the poster from the conference, um as you can see, I can't make it any bigger, but as you can see it's very nice, it was December, 12th and 13th of 22, and there were a host of people on different topics, uh ranging from developmental bio standard, developmental biology to mathematical, biology to engineering and other other areas as well.

B

So the diversity and complexity of shape and uni multicellular organisms has long been a source of Fascination and interrogation since the seminal work of Darcy Thompson. The study of the emergence of shape, so-called morphogenesis, has been strongly influenced by the concept of emergence whereby complex pattern and shape can be explained by a relatively simple mechanical and mathematical laws.

B

A

B

Darcy win or Thompson's book on on growth and form uh he talks about different mathematical models for looking at animal morphology. uh Looking at seashells looking at other organisms with very regular looking phenotypes and I mean that in the sense that there are, you know, there's a high degree of modularity a high degree of patterning and you can actually analyze these through different mathematical tools.

B

Different mathematical functions and different types of mathematical transforms that he applies one of the most famous images he has of a fish that he takes and he puts on a measurement grid, and then he takes a measurement grid and warps it uh to match that phenotype. The landmarks on the phenotype, like you, know, different places like the tip of.

A

B

The tip of the tail and so forth, and then he uh he warps the grid again to fit other fishes. So you can look at uh variation between different fish species by continually warping this grid and then registering the mathematical transform that that results in. So it gives us some indication of uh changes in shape over Evolution and you can do the same thing in development. So you introduced a number of tools that were the book was a bit ahead of its time because it didn't really.

B

um You know the the computer techniques weren't there yet, but now that we have high-powered computers, of course we can do these types of analyzes much more easily.

B

um So this this is kind of the inspiration for this uh Workshop. In parallel, the rapid for aggressive biochemical characterization of The Regulators of cell signaling is open the possibility to compare developmental programs and dissect the molecular basis of evolution.

B

So we can take these types of mathematical transforms that describe differences in shape, but we can also look at the biochemistry of these changes and we know from Evil Devo that these changes are due to changes in, say uh the promoter of certain genes or the changes in gene expression over the course of development, and it results in in size, changes, shape, changes, shape, Transformations and so forth.

B

So we want to be able to merge, mechanics and mathematical laws. We also want to merge the molecular aspects of morphogenesis, and so that's what this Workshop was about, and there are a number of speakers from different disciplines here.

B

The discussions and talks that were presented here covered a large range of approaches, mathematics, fluid mechanics, soft matter, physics, genetics, Evolution, developmental biology, epistemology and so on, and so a lot of the things we talk about in this group, um and it was a very, very interesting um set of talks.

B

There were a lot of open questions that were raised that weren't really answered, but the point is of course in one of these types of events is to raise a lot of questions uh so this it also showcased the diverse set of organisms from mammals, birds, reptiles, insects, choanoflagellates plants, algaes and bacteria, so just a wide range of organisms from single cell organisms to multi-cell organisms uh that exhibit very complex behaviors.

B

So this is a really interesting uh set of talks that sort of covered a lot of diff a lot of territory in terms of these kind of changes in morphology, over development and evolution, different modes of development, different areas of the tree of life and so forth.

B

So uh it was so the morning sessions were an unconference and they were live in Paris and as being a virtual attendee. I didn't get to attend these, but this was interesting because it really kind of allowed people to interact so I missed this part of it um and it is probably pretty productive. I. Imagine I wasn't there and that's where a lot of these open questions may or may not have been answered.

B

So you can see that they have the Whiteboard, they were making nodes, they were, you know, kind of brainstorming things, and so that that's something that we could replicate in this group um in terms of our own interests, um so they had a round table the first Roundtable address constraints on the evolution and shape, Evolution or shape and how to reveal them experimentally.

B

So this is uh this idea of taking what Darcy Thompson talked about and looking at that more a more modern perspective, evolutionary and developmental constraints on shape, for example, what kinds of shapes are possible, given certain conditions, evolutionary conditions, developmental conditions and so forth, and then how to reveal them experimentally so how to do experiments that you know intervene in that process to show some of these possibilities or show how these things are constrained.

B

So how can an organ evolve from one shape to into another? Are there limitations regarding shape innovation?

B

Those are two questions that are related because we know that, like in in terms of like the drosophila wing that you can have shape Innovation, you can have changes in the shape of the wing, but you can't necessarily have novel shapes for wings, sometimes uh their developmental mutations that modify the shape of the wing, but there's certain shapes that can't result in a functional, phenotype and there's certain shapes that can't be derived from what what is there available to the organism?

B

Learning the different distribution of organ shape in the Morpho space. So they talk about the space of possible morphological configurations using inter intron intra-specific variability can reveal such constraints by looking at the non-occupied zones in the Morpho space. So our organisms occupy this morphle space. We see some of these variations, some of these combinations that are more common than others, and some of them you never observe in nature, and so those non-occupied zones as it were, are the areas we're really interested in understanding exactly what those constraints are.

B

Why they're the way they are so um these the cause of these unoccupied areas can either be selective pressure or the funneling of evolutionary change by developmental constraints. So this is a very interesting topic. This is something we might return to in talking about uh sort of natural selection and development, and this tells you a lot about like the possibilities of what evolution can produce, what it can produce from a certain starting point.

B

The second discussion was centered on the emergence of multicellularity, so this is where you have single cells and then they divide and they start to live in colonies, and then they start to live in even more closely associated groups that are multicellular groups, and so there's a lot of work on yeasts that, where you have clusters of yeast cells that form these multicellular Aggregates, and so you can look at multicellularity as this sort of uh transition from a single cell to a multi-cell state of living.

B

But in between there there are some interesting structures that have uh that emerge, and you know to understand this transition. You have to understand some of those intermediate States and what it means to be multi-cellular to live in the aggregated state. So by comparing the motor multicellularity, mono aggregative the signals conditions triggering aggregation or dissociation, which means to blow them apart and make them live separately and the components that can structure and organize the aggregates adhesion contractility The Matrix.

B

So all these things are play a role in in forming these Aggregates and we talk about them with respect to cell uh more, you know, changes in cell morphology and even some of the tensegrity stuff we'll talk about today, um a very complex picture emerges with those sorts of combinations of strategies found in nation nature, outlining again the diversity of evolutionary paths leading wealthy cellularity.

B

So we've prepared this table where kind of shows some of these uh taxa that we've been that they talked about that were featured at this um conference and some of the states of living. So you have unicellular Quantum, multicellularity, aggregative, multicellularity, sexual reproduction, symbiosis and so forth, and then in these different groups is taxonomic groups. You can see whether they exist in that state or not. So this is a nice way to analyze this type of diversity and to see these non-occupied zones.

B

Okay, then there are the afternoon hybrid sessions and that those are the ones I attended virtually and those were also held in pairs very interesting talks here. uh This was a more classic set of talks where they focused on different topics and they ranged across a lot of different areas. So the first one was a historical overview of the evolution of approaches used to understand development, and this focused on mechanical constraints and shape and how people have done experiments to demonstrate those things and to bring that into the modern era.

B

uh Then there were, there was a there were a number of talks on the cephalic Furrow in drosophila embryos, so the cephalic Furrow is this uh structure that moves across the uh embryo and starts to give it. Some sort of shape uh starts to allow for folding and other types of uh morphological Transformations and so understanding that, as maybe just a mechanical phenomena or understanding that as something that interacts with gene expression and is caused by some sort of Developmental program is what they were kind of getting at in some of these talks.

B

um But we also had talks on Plants. We had talks on petunia, petals and sepals. We had talks on how mutants can be used to disentangle some of these different issues. So, for example, the furrow is it controlled by genes or is it controlled by mechanical processes, the same thing with plant growth and development? What kinds of G genes are involved and if you knock them down, what are the effects?

B

There are also other types of quantitative studies on different types of mechanisms, so mechanisms such as embryo compaction and mammals, how these types of mechanisms can be interpreted and understood uh this talk here. uh uh There was a talk. There are a number of talks on topological transitions um and how those things are analyzed and understood. So there are a number of there were some studies of organoids.

B

There were some studies and epithelial cells that allowed us to look at some of the changes in shape and the Transformations and shape and how those can be understood um there was another uh set of. There was another talk by Jose Biko who actually works with these uh inflatable shapes.

B

So the idea is that you can build these models, um they're inflatable models, you fill them with air they inflate and they take on a certain shape and the idea is to mimic uh these morphological Transformations using a very simple physical model that doesn't have any genes and it doesn't have any other uh biological structure. We usually have in biological systems, but they have the shape. You know the conformation of shape and they can, you know, constitute the soft active material that we can play around with and see.

B

You know what the constraints on shape are when they're, under a certain stiffness, when they have a certain set of constraints applied to them. So it's very interesting work. um There was a more more generally. There was a focus on soft materials or uh biological systems and soft materials, and, of course, biological systems contain soft materials. But there's this idea of soft materials and living matter that we talk about the meetings uh that they emphasized in this set of talks.

B

So there you can look at local differences in in growth. In the the inflatable shape model, you can use inflation to mimic growth in soft active materials. You can mimic changes and growth changes in shape by different uh sort of techniques, uh and so you can do this in culture and tells you something about what these shapes are and how they how they make their changes.

B

uh You can also we also look at insects uh looking at different gas relation and different fly species, so identifying genes that uh explain differences in in more uh morphology, morphological changes between species, but also within the common ancestor that resulted in the different species.

B

So it's looking specifically at the mode of mesodermin, vagination cell elongation and the requirement for cephalic Furrow formation and then finally, there was this talk for the first day where they were looking at differential, mechanical properties of tissues. That explained the morphogenesis of the jellyfish Canal Network. So this is a network within a morphology that is explained by different types of mechanical theories such as cracking Theory. So you can look at the jellyfish Canal Network as a mechanical structure, as well as a biological structure, and so with more of the same.

B

In the next day, there were some other talks on fluid mechanics, applying fluid mechanics to Chick gastrulation, looking at different craniofacial shape, Evolution patterns and across vertebrates, neurons, finches, bats and crocodiles.

B

There was a diversity of you know, different types of changes that resulted in these different morphologies understanding that morphle space and how those changes happened, um also looking at different swimming strategies and Collective behaviors in volvox and gonium and Kelly Dominus with these are all single cell organisms, so they're very different from our uh context of like flies and mammals and other types of vertebrates and invertebrates, um and so this just provided a nice contrast to some of those systems.

B

uh Then we had a set of computational and theoretical considerations on the concept of autonomy and some of the core principles that are play: a role in autonomy and how that relates to these issues of growth and development.

B

um So and then they had, uh they talked about more physical models and applying those to viscoelastic deformation and other types of uh morphological uh problems such as a maze growth against the physical obstacle.

B

So looking at how growth, not just development proceeds and some of these physical models that might come into play and explain them and then, of course, in East, we talked about the long-term, experimental, Evolution experiment of little Ratcliffe, where he's evolved, yeast over many many generations and he's more specifically he's been able to evolve these uh Aggregates of yeast cells and how they form these multi-cellular proto-multi-cellular colonies and other types of structures.

B

So it's very interesting there's also some very interesting physical work or physics work that they've been doing with that biophysics in the sense of you know. How do you describe these multicellular Aggregates and their function?

B

And then finally, Stuart Newman provided uh some interesting ideas about the concept of dynamic pattern modules. Integrating Gene regulatory networks into models of physical and spatial constraints and development and how those things relate to evolution. So it's very interesting stuff going on there like I, said again, I'd, be very interested in exploring some of these themes. Further there were some questions.

B

I had about some of these topics and it's probably uh Grist for another set of discussions in another meeting, so yeah, that's great, um so Susan you had a thing that you were gonna talk about.

C

Sure I have a new house guest. You can.

B

D

C

D

Oh, at least it's not on your keyboard.

C

Now she needs my papers, I print them out, so I can sit and read them nicely and she likes to play with them. Oh should I adjust them before you yeah, so I have an excuse for my homework. The cat ate it literally.

D

Yeah the cat ate my homework. Yes,.

C

Anyway, I do have I, have a PowerPoint thing, I'm, making um it's too long for today, but I can show you, the cell biology part yeah, yeah, um okay, so I just have to figure out how to share my screen um there.

C

Okay, ah anyways I, just wanted I, wanted to see what you thought. Okay,.

D

C

Of there so I've made um made this I've got here's pictures of some epithelial cells and they included um that contractibility with actin and myosin. These are the stress fibers. Then they've got actin in a tissue on how it's connected.

B

C

They've got prevention, intermediate filaments.

D

Is this for presentation somewhere? Yes, yes, okay, you might emphasize it that one, that's labeled, cortical, acting architecture emphasize that that's at the apical surface of the cells. Okay,.

C

um Top view yeah.

D

C

This is a top view of actin intermediate filaments and microtubules.

D

Now wait what what's the view of microtubules that top of you or beneath the top? Well.

C

um It just said that this was the apical view that um an image of microtubules are.

D

C

D

C

D

C

No, it didn't say how deep.

D

It didn't say how the okay uh remember best yeah besides paper, where she found apical microtubules. Yes,.

C

D

Okay, I think it depends on the self I. Don't know if you can get an image of those, because she did it before the fluorescence techniques.

C

Well, anyway, this the actin is in red and the microtubules are in green and yeah yeah.

D

I'm just saying you can't get the vertical component.

C

um This is looking down on it, so yeah I know I know you know, but if we don't have a depth, I know um so here here in the acting is in red, and here it is in green different dies um and then the next one this this is showing where the actin and the myosin is in a T cell, so they tend to form a ring around the outside surface is.

D

That an isolated self, not an epithelium.

C

This is yeah, it's a T Cell, so it's a blood cell.

D

Is it a flat cell.

C

um They're a me boy, so they've.

D

Chain shape, so why should it never ring or is it yeah.

C

um It doesn't show up well that this is a slice- oh okay, okay, so the actin and myosin is surrounding the outside of the ball.

C

D

C

Yes, I need to say that somewhere in here, um this is even more confusing there you go. This is microtubules and actin mixed and I've got way too many words here, um but um the actin I believe is in red and the microtubules are in this blue color, so they associate with each other. This is not not the best image, but it was interesting because they've got actin associating with tubulin, at least in in this image.

C

D

C

I, don't know I, don't think this is in a cell.

C

Oh actually, all right I have to go back to this paper, so I I put it in here like this, is sort of not quite finished um and then another top view of um cells, and if you have a close-up, you can see where the the attachment attachments are, they kind of have corners.

C

So this is like the model of the cell. I was kind trying to make card. Has um six sides we've done attachments that aren't the corners?

C

It kind of looks like this: it is yeah, some of them are squares, some of their pentagons, so it shifts yeah.

D

C

So this would be only like 1.2 times.

D

Why is that? The.

C

C

Myosin complex.

D

Oh, so the stain is the same along the edges as the corners.

C

um No there's uh this red, um yeah I knew somebody was going to ask this and there's a red dye along the sides and there's the green dye at the corners. Oh, so I'm, assuming um that the act of myosin complex, is green.

C

We need more detail on that slide.

C

But um yeah the myosin and actin just kind of act like a muscle and they pull on the actin to pre-stress it, which you need in a salad, it'll just collapse.

D

Again, this is all of the apical surface. Yes,.

C

So what I thought was all the apical surface now this is a side view. um I, don't have a good side view as an actual real image. Okay,.

D

uh Okay, so now it's a stupid question. Okay, why isn't the same thing happen at the bank Eastland.

C

Because there's not there isn't this uh acting myosin a ring.

D

ah No, why don't you get a ringing and.

C

Why don't you get a base here? Yeah, oh because this is an epithelial cell and they have a basement membrane and they like to attach to a basement membrane and when they do this, then they don't do that.

D

All right: well, how does the asymmetry happen.

C

Can I answer basal asymmetry and an epithelial cell? Yes,.

D

That's the question so don't answer the question. I, don't know the answer either so yeah.

C

Well, uh they tend to attach to this this ring of actomycin around here with Ted Heering in between here and then the basal part of this is attached. Okay, um my integrands, so you have kid hearings.

D

So is there a difference between Heron or Junctions and stuff at the bottom, uh in this attachment properties to actoon and myosin.

C

Okay, I'm not sure integrine complex, but it has some. It just has attachment, abilities, I believe and the extracellular Matrix kind of determines the stiffness of that attachment. So if the extra cellular their Matrix is stiff, then the attachment is stiffer is very dependent on The extracellular, Matrix symbol, okay, real cells.

D

And by the way for uh Axolotl the proportions of Rock, in other words, it's things that neural plate stage. It's about 25 microns across and about 100 150 to 100, grinds vertically.

C

Yeah, um okay, what's the ratio.

D

Let's look at something you better look in my old papers or something I think it's about 25 to 100., okay,.

C

Yeah, because there's there's these are just short, um you know you, you like these are like liver cells or something yeah you're.

D

Showing these with squares, but an axolotl will be long. Rectangles.

C

Well, you can have long rectangles, and yet you can have these boxy things, and then you can have the really flat ones. Oh okay, like the flat ones, are the skin cells, okay and they have a similar attachments um anyway, I had a I have a picture of that somewhere, um but not in these slides.

D

But you can talk longer.

C

Maybe I need to be making notes here. Yeah.

D

Okay: okay, look it's just before the presentation in a card typing culture.

C

All right yeah well, this is this- is very rough. This is sort of okay, I need State, different shapes.

C

And more explanation: anyways, um so they've got cell to cell Junctions. Here, they've gone over it in a fairly great detail: oops! So, oh um you can have different um see: adherence Junctions, desmosomes, femi desmosomes, focal adhesions.

D

Which Junction allows transmission of ions.

C

um The they don't have them in this Gap Junction. So there they are yeah Gap, Junctions, okay, yes and GAP. Junctions are not attached to the cytoskeleton directly like they're, not attached to the actins or the intermediate filaments directly they're just attached.

C

um They just kind of make a hole through the cell um Glen brain and you can pass proteins and well I. Think you can pass proteins. I, don't know it's a small pore I mean definitely a good ions across that and GAP Junctions occur in a plate to like the heart.

D

Okay, which kind of Junction is involved in Axolotl Turtle plate in a neural plate, in other words, the ones for which we found these I'll say splitters.

C

Oh okay, I haven't thought about that, but my guess is: there's some tight junctions and some anchoring Junctions there 'll be um okay. They have these X's for tight junctions to keep things um safe because they're, an epithelial so and then they'd have of that cat hearings. Attaching to the um actin octomycin ring explore here.

C

And they haven't included the microtubules, so they're missing something interesting. Okay,.

D

The microtones attached to the Junctions or not.

C

Attached to the Junctions I think they're, um just in in here in the act and sometimes attaches to the microtubules. So just a part of this complex, that's my reading on it, but I took a whole mini course on what was it um on? Was it edx or the other one? Oh there's a mini course on Cell cytoskeleton, and they these um actin filaments, have an interesting um branching that they do so they can hold each other up. So this is a. This is a brand new network in here foreign.

D

Many different configurations for self-state Splitters: do you remember that yeah okay uh is, can can you modernize that bring it up to date in terms of all the instructions and whatnot.

C

D

D

You know, for instance, there were microtubule rings that parallel the uh accent rings.

D

In some configurations.

C

Yeah um I assume it all depends on the cell and the the um oh.

D

Yeah yeah they were different from they were from different kinds of cells. The question is: can you update that information? Oh.

C

Oh look at it because I've certainly taken that course, and it's interesting. If you um on your cell with a high Force, you get more um more actomyosin happening are developing okay um and it has a different branching, because.

D

Hollow Branch, yes, we gave references for each hypothesized cell space splitter configuration those. It would be possible to take the papers that we decided and do a forward search up: okay, yeah, okay, that might tell you whether anybody followed up and get more detail, because we certainly did not have the information about Connections and Junctions.

C

Yeah well, it's interesting, um and my thought is, is that um you know how you have the microtubules um held with the elastic bands out to stiffen it. You might be able to do that with the actin too, because it is a um it has branches, so it stiffening itself in some ways.

D

Okay, uh is there an intermediate filament, bringing the parallels the uh the actively.

C

So, let's see they had I think this is go back to the first picture.

C

Of course, this is where they have the intermediate filaments and that's around the nucleus in this case, but um if I go down here, further I'll just continue here, um so this I think this is a better diagram because it shows it more in in 3D than here. Are your intermediate filaments.

D

Okay, if you remember Chris Martin's work where he found intermediate film movements in a ring, paralleling I think it was slightly below.

C

And parallel the actively okay, so there's oh: where is it stable adherence Junctions? So intermediate filaments? Are the blue.

C

Actin is around there's, definitely an acting ring there and then microtubules.

C

But um I still think this is maybe missing something I'm I'm, not sure yeah we're interested in this, because this is a soft surface, yeah um pipe Junction and stable adherence Junctions along the apical surface. It looks like intestinal building yeah. It does definitely does and then they've got one here, that's on a stiff surface and they show the integrands here twice as many attached to the extracellular Matrix along here, and they have the actin fibers. The stiff, actin fibers are all on the basal particle.

D

Or the ankle side side is turned upside down. Yes,.

C

And then then they see these are weak and unstable, but my thought is: is that that if all of this is stiff, this is going to be um more unstable. This is going to naturally break more just because of the mechanical nature of it: okay, yeah anyway. Let's hope this is a confusing cell biology. I've been looking at and I have a stack of about 100 papers and I need to read through them, and it's just it's not clear.

C

Okay I think that they can make a stress strain curve, why no, they took the stress and they divided by The Strain, and you saw it as a variable anyway. Why? Why.

D

Okay, well, no sympathy on another subject: I just collected 700 papers, okay, I'll go through I.

B

Had a question about the soft and stiff surfaces realize two slides the.

D

C

B

A looks like you have a network of uh uh actin and microtubules, but you also have the surface the thing that it's embedded in, so that that's basically transferring forces into the network or what's the nature of that, sir, that material that it's embedded in is that uh play a role in the stiffness. Or is it just the configuration company.

C

Their friends are all in the stiffness, yes, yeah.

B

And so it's just the geometry, or is it the sort of the nature of the material.

C

Yeah, you need to know the stiffness, the stiffness of the ECM extracellular Matrix or the substring that the cells are on. Okay,.

B

C

Very important, okay, um so I I did state that here you need to know the pre-stress like how that uh myosin is stretching the actin and you need to know the extracellular Matrix, and it's also good to know where the um actin ring is in the cell.

D

um So what other Factor Susan yes I know what the surface is: hydrophobic or hydrophilic. Oh okay,.

C

Yeah, well, they usually make them a hydrophilic yeah.

D

But uh did you have you looked at any verse study where he made little squares that were hydrophobic and hydrophilic and the silk about the size of itself squares.

D

C

Okay, well I um I can look at that here. They've just changed the.

C

The stiffness of the vasal membrane, so this is instead of the extracellular Matrix they're, attaching them to things like glass and and um gel yeah, and they pre-measured the substrate or the extracellular Matrix substitute.

D

And again again, you have to control for hydrophobicity, because glass is typically hydrophilic unless you coated something well.

C

I think they did, um they were careful about it. Oh.

D

C

Yeah that's. Why kind of why I like that, this article and it's an older one, 2006.

C

And I like that, they had a graph, so at least they had elasticity along uh one um one line and it's a differentiation markers along the other. So the blue is nerve tissue and you can see it. It occurs when the elasticity is less and then you have.

C

um This is myod what is outside of muscle, muscle, yeah, muscle and green, and so it's in the middle, it's kind of a middle elasticity, and then you have bone, and this is bone here at the higher end and I, maybe should have included it. I have uh paragraph showing all the tissues in the body and how they of what their elasticity is, and it lines up perfectly with this graph.

D

Okay, these well, they use the same kind of cells for all three cases. Yes, Madison.

C

Kind cells, I, I, don't know, I look for the species and there's no species that's listed. So this is how I'm going off Mysteries the.

D

Universal app yeah God, it's annoying. It's a giant mouse yeah.

C

I think they're a giant mouse or cue. Maybe it's human and they didn't want to say yeah, but anyway yeah. Maybe.

D

It's human fetal tissue.

C

Yeah yeah controversial human fetal tissue that would do it. Yeah I, don't know anyway, it was 2006 and that I didn't know this existed before so I was in awe. So it's been included here.

C

Yeah I should yeah I need to do a forward. Search. I haven't done that see. This is color polls, this presentation um and anyway, so they. This is um two ways of measuring things. One is with beads and the other one is with uh micro, pipette aspiration and they do get um sort of similar graphs, but there's there's slightly off there. This. This is three stars, and this is cortical stepness, so they're similar. So I, don't know if they're comparing apples to oranges, but they think they're comparing different types of apples. So there we go.

D

Okay, uh just one one comment: there do you use the word tense cells uh would suggest to some people you're doing a psychoanalysis and.

C

C

D

C

B

First cells, yeah I.

D

Like that, though, oh okay, yeah, okay, fine, that's that's not.

C

Going down yeah, okay and I need to put these are beads, and this is micropipette. I need to put that in there.

C

They're making notes.

C

And that's slide 14.

D

Okay, Susan yeah. Are you familiar with the old experiments where you put an oil on water and it spreads out and you guys shouldn't thickness of the oil layer that would be from high school? Maybe.

C

Oh um I sort of know about it.

D

Okay, it was used to measure the thickness of the layers of oil. uh Now, if you did that it was epithelial cells, they'd be floating on water and so to speak, which would be a very low stiffness, medium yeah, okay, just I, just throw it out. I, don't know.

C

Okay, well it, the very little stiffness will turn into a blood cell.

D

C

That's what they say: they turn into blood cells if they're on fluid, they turn into blood cells. But if they're on a compress or pressurized fluid and being um Under Pressure, then that does something else.

D

Okay, um hold on a second I'm going to copy of the scoop. Can you send me that paper.

C

uh The 2006 one here this one, the one, no the one with the triangle: oh the triangle, yeah. This is neat. They I like this because you don't have to move anything. You just put yourselves down on on a triangle. Oh you put them on the train, oh yeah, and then the triangle Clips are at a higher tension of where you can see where, where your mesoderm develops and where it doesn't well,.

D

It's still like that, because I'm I'm working on triangle, myself.

C

Okay, they have a interesting um what pressure that occurs at the tips. Okay,.

D

So, okay yeah, so I can I'd appreciate a copy of that. Whatever page that track in the picture came from.

C

Okay, all right, thank you anyway. I like this. um This is measuring, I, believe tension, sleep between the cells and the rest of these have been the substrate measurement.

C

And then um this is again um looking at integrands from if you have a mutant that doesn't produce integrands or or the fibronectin, for whatever reason you gotta, you have problems so in neural tube closure. This is a very sensitive part of embryo development.

D

C

As we're looking at the tail or the you know, the start of the tail I believe- or maybe it's the whole thing that this particular picture is from from the tail closing um anyway, even the shape of the neural tube changes, if you don't have the integrins holding it to that extra cellular matrix.

C

So this is this is wild type. So this is the way it's supposed to look, and this is the mutant without the integrines attaching and you can see, there's there's a difference in the cells along the line.

D

Again, does that go to birth or no.

C

I I believe it does, but I'm not sure, because this is 18 and it's almost ready to be born, and this look distinctly different from that.

D

C

And there's the Spina Bifida right there and it's got a tail defect, um but this is the tail doesn't grow huh so anyway, that's uh it's.

D

A problem, so the top is Mouse and the bottom is human.

C

C

Is okay, at least I know that oh well, and so that's kind of the end of the cell biology and then I I get into starts thinkers. Why can't biologists do this with some of these um lovely experiments.

D

Because they don't want to study any physics.

C

Well, at any rate, this is how much it stretches along the x-axis and how much force is being created on that the y-axis yeah. This is a standard in the industry.

D

C

And some people have measured stress, strain and tissue so and I think this is a bone anyway. um Tissue always has this toe. So it's sort of a j shape, it's not toe tissue. Oh it has a toe and then a straight middle part and they call it a j. But it's.

C

More examples of what they would consider a j shape hearing muscle- and this is in brain.

C

So it really does have this type type of her.

C

Oh I'm, guiding an echo.

D

Okay, this is external stress.

C

Yes, this is best applied externally: uh-huh, yeah, okay,.

D

This is show hysteresis, or does it come back to the Curve.

C

I think the brain might have shown hysteresis um and there are is sometimes hysteresis, and sometimes there isn't, depending on the tissue, and if these, um the brain in this picture, I know it's dead tissue and it will be different from live tissue.

C

So when I um I'm going to be doing some experiments, I really want to do experiments on live tint you because it's what actually works in the living system.

C

You can microwave and kill it measure the difference in tissue experiment.

D

For what yes, you two can come up with a definition of Life yeah.

B

C

We go and then and then there's this I.

C

um If you, if it depends on the tissue, how long you can um apply Force to it and how much force we can apply to it.

C

So the time is important in here and the amount of um a force you can apply to it is important and um a quick Force is is different than a slow moving Force. So there's all of that that you have to keep in mind when measuring things.

C

So yeah, so this this is kind of measuring tissue. Oh I forgot one.

C

Alive or dead there we go it's some of the things you have to keep in mind.

D

Okay, the dentist presumably stay in the live region, and so what dentists, with their braces and things like that, by moving teeth, yeah.

C

Yes, so they want, they want these uh relaxed and relaxation curves to occur in.

D

Your mouth okay uh comment: this is a general property of disco, elasticity.

C

Yes, all of this, um especially the relaxation and creep curves, yes measure the disc elasticity.

C

And some of the frequency response does as well. You just have to get the right frequency going and quite often and say, mammalian cells. It kind of their natural frequency is, is um the rhythm of their being eating heart? Whatever God is? Oh, okay with people is 60 times a second or I mean a minute sorry, and with uh birds and other small things. It's it's quite a bit faster.

D

The hummingbirds is the extreme how many, what look up hummingbird heart rate for the extreme? Oh yes,.

C

Yeah, that's right! Well, even most like it's, it's faster, they're, smaller and.

C

um So what is it? Oh, then, there's different ways of modeling tissue and the one I managed to get myself into sort of by accident. Was this tensegrity.

C

So there's a red intention.

D

You can see you can see a rotating structure there.

C

We go yes, rotating structure.

C

And um this is just from Wikipedia: they had it on there a little gift, um and then this is the tensegrity that I'm working with it's a six-sided one with um six rods and a um what how many strings three times that many 18 strings, yeah yeah. So here it is and there it is in Matlab- and here it is in console and um I can go back and say this. The top doesn't line up with the basal part of this, like it's got a Twist to it.

C

So in this one this this is the one that's straight up, and this is one that's twisted and I. Add this straight up one made because you can't do a tissue with the Twisted one. It was impossible and console. It is difficult to do anyway. I'm sure, if you you made one like this and then kind of Twisted, the whole structure that might might work, but.

D

Okay, Susan. Basically, a twisted structure is also chiral. ah Yes, yeah, okay. So what you're saying is that epithelial tissues our number of chiral well.

C

They're they can be because uh or they can have a swirl in them like the pattern of hair growth for.

D

Instance, oh right right.

C

Can be nice, but these are malleable. I mean they're, essentially elastic their skin, so you could have a Twist in them now that there's some more study I mean just looking at this just brings up issues.

D

Yeah, you need to scalp a few kids.

C

Get experimented.

D

On bicep worlds in the world, most kids are girls.

C

Yeah, oh there's squirrels everywhere, burnt South whirls, oh really, okay, yeah um anyway. This is just my experiment and it seems to go over a really broad range of of forces like it's just quite amazing.

C

um This Twisted structure stands up better than the straight one. So I did a couple. Experiments.

D

Okay, is there any? Are there any observations of the structure of epithelia at the edge of a wound.

C

um Yes, there are.

D

There are one okay, it would be interesting because there you have an imbalance, yeah yeah, yeah,.

C

And I I think uh back to myosin, which is the string in this case. um They just um and there becomes a gap, I think they join each other and then close the wound, or else you get something migrating in I had some in an interesting paper about migrating cells.

C

D

uh 10 years ago, I attended a lecture of making holes in the ears of rabbits yeah.

C

D

um And I try to do a calculation of the forces involved and my conclusion was that something's going on strange because the cold closes up. If you make a hole in the rabbit here, it eventually closes up, whereas if you make a hole in the sheet of rubber, it gets bigger.

C

Yeah well they're alive and I I think they form an act of medicine that causes it. You got your actual myosin muscle, type, movement and I think it closes like the gap. I'm not sure about that. But that's.

D

One healing about the Steely, it might be a a source of information. Yeah I, don't care if anyone's done. It done to microscopy of it. Foreign.

C

Then I ran into this paper. Somebody had done uh with with this. um The three struck: oh right ready, so it's kind of nice. um It shows the movement here. It only goes until the strings touch or the rods uh touch so and when um you apply a force it winds down, so it becomes a different height, but they did this.

C

um They did it with elastic strings and you've got this sort of a curve. Oops.

C

And if you do it with stiff strings, you get a J curve, oh well, the J curve. This is more like what a cell is, and it has a J curve like tissue has Jacobs so I'm, assuming that the um uh Stephen lavender was right. Of course he is about this, and your act of myosin ring is quite stiff and Dr ingber says it's like a hundred a hundred gigapascals.

C

There are 100 megapascals, oh.

D

Did he get well, he got past using strictly elastic, uh insecurities.

C

I think so, because he states that that actin bundles are are very stiff.

D

You can see the kind of hard time I get students yeah.

C

Anyway, this is in compression, and this is intention Susan.

D

Survived me somehow, yeah.

C

I I don't mind your questions, they, they add, definitely add things. So he made a call a taller. He called it a slender model. uh Well so, and it it oops, it has different J, curves and um anyway, I need to hurry up with this I'm. So ten segregates need to this is from Stephen Levin. They need to be J shaped and the pension uh elements need to be very quite stiff.

C

um And this is a single blood cell, it's not in a tissue and it has a more rounded shape and this is a rounded shape. But it's packed.

C

um Packed cells- and this is more inside of the body.

C

So it's called honeycomb wrong good for me, um so the epithelial tissues are more of a honeycomb shape and inner body tissues are more like these uh isohedrons um and are packed like bubbles, so there that I'm sure that comes from you, dick so anyway. So there is I love this. This thing that was done with this.

D

Yeah I didn't expect demonstration.

C

Yeah, oh there. It is anyway, um so any questions or should I.

D

Noticed Italian show, but it no longer looks 3D.

C

Well, I have these ones up here: do they look 3D yeah.

D

uh Yes, because you can see what's in front of what.

C

Yeah yeah, if you, if you weren't an artist, you'd, be going yeah.

B

C

These last two, these.

B

Last two slides you showed these last two slides you showed were or the last two before the concluding slide were interesting, because that.

D

Gets into biological sounds, you can see it on the uh on the thumbnail. It's not rotating okay,.

B

D

Oh all right, it looks.

C

Flattering yeah, it looks the only reason why, uh where is it? This doesn't look flat to me is because they of the lines here right right right.

D

B

C

Anyway, that's I kind of what I put together is, but it means more explanations. Obviously, okay, yeah.

B

D

Last two slides.

B

I wanted to go to those just to like look them over a little bit more.

B

In the in the talk like the ones, the biological cells or whatever yeah so.

C

That one, oh, your blood cells, yeah, so.

B

That's that's interesting because that that proposes some really like explicit uh structures for like a real cell, so the red blood cell, yeah.

C

B

No, that the red blood cell, one where you just weren't, let's lay down at the end: okay yeah there, oh yeah, that.

D

B

Stuff yeah, so that's that that really shows some explicit like claims about what they look like, like with a red blood cell. What the network should look like- and you have like, so this is from this paper- structural, multi-disciplinary, optimization.

B

D

What are the molecules, what are the blue molecules in the red molecules.

C

You know the blue molecules are the stiff elements: okay, which are I, don't know, you could say they're that well, they're, stiff elements right and the red ones are the cables yeah. But what are they? What are they? Yeah I? Don't know whether they're stiffened, microtubules or or stiff and active at the moment, yeah.

D

Because uh my old understanding of red blood cells is that they were structured by a network of some kind of other molecule, not not a standard cytoskeleton molecule that was adhered to the membrane. Oh.

C

Okay, well, they they've lost their nucleus. Yes, yeah, okay! Well, just just in general! That's that's! Why.

D

There's a huge literature I'm trying to explain the shape of red blood cells: oh okay, I'm going back at least 40 years. Okay,.

C

um Yeah, no I, don't know if they explained exactly what the Bars were.

D

Yeah yeah, that's yeah what yeah, what what Bible School do? They represent yeah, I I, don't know! That's why he has because a lot of the models I saw 40 years ago only involved the surface.

C

Oh okay, well, I'm I was trying to do tissue. I realized this is a tissue, but it's you know more, like a single cell. So um it's just it's here for contrast, because these are, um if you develop um Amazon Prime cell on a fluid surface, you'll get something like this.

B

Okay, yeah it's nice because it describes the shape and it says why you know what should we expect from that shape and then you can go back and say well. Why is it structured like that and what's what's you.

D

Know maybe it isn't the same technology.

D

It looks like a sort of almost random yeah evaporation.

C

Yeah- and it could be like it doesn't- have to be.

D

Okay, because that raises the question of Dynamics, suppose you made a structure like that of microphobules, but the individual microtubules were undergoing uh Dynamic instability. You know what that is.

C

Yes, and that one might help it when it goes through um one of those little micro.

D

D

B

Heard of it, yeah, okay,.

D

Phenomenon is simple: the Microtel will grow slowly or it collapses very rapidly at about 10 times faster than it grows. So it's continually in a process of growing and collapsing.

C

That happens in the heart.

D

Okay, so if you made a tensegrity structure of stiff elements like these, but each one is not a fixed lens, but scrolling and collapsing does the overall structure remain intact or not, and.

C

It needs to fold to get through micro, capillaries.

D

Oh yeah, of course, and in fact it becomes cup shaped.

C

Yeah, so it has it.

D

Has to do that's a different phenomena.

D

The instability, if you look at the dynamic instability of microtubules with this number of microtubules, do you still retain the structure, even though the individual microtubules are growing and collapsing rapidly.

C

Apparently, some of them Canadian and dying uh well stabilize the microtubule.

D

Well, that's stabilizing, but if you, if you, if you're, let's say not stabilized, what does the structure does the overall structure? Nevertheless, too many Tech?

D

Now, that's that's the question that could be answered by try taking a model like this and adding Dynamic instability to it. Yeah, okay,.

D

And uh I think you've come up with a critical number of microtubules at which the shape is.

C

Retained, oh okay, yeah, so that's another study and then you want to stand up.

D

uh It's probably worth it off my life, yeah.

B

And then, finally, that.

D

D

Most work on Integrity assumes a fixed structure, but the dynamic instability of microtubias of microfilaments I, don't know about intermediate those.

D

Questions, whether or not how many does it take to have a stable structure, even though the individual components have been continually changed.

C

It is a good question: yeah.

D

And I think it'd be done by simulation now. Oh.

C

Yeah, it might be.

D

C

Yeah, um maybe console might be up to it, I'm not sure yeah. First I have to get this little thing to work. Yeah.

D

C

D

Because you just have, all you have to do is increase the length of the of the blue elements here or uh decrease them individually. Oh okay, here independently, okay and then.

C

Try it out yeah, try it.

D

C

D

I well, it gets a little more complicated because the rates are also dependent on what's called diffusion limitation. In other words, if they have to attract monomers of uh of microtubules tubulins, then they deplete the mic. They deplete them in the neighborhood of the growing app well.

C

If they've collapsed, the.

D

Molecules are there: if they collapse, they will produce them, they will, they will produce free, tubular, tubulin and if they are extending, they will create a Zone around them of depletion of the tubular. Oh yeah, that's true, so that makes the model a bit more complicated, but first order.

C

So exactly well: I've I've got some of that trouble with oh I I'm having trouble with my model because you can go from zero, I think to a thousand Newtons are with this and the thing still works and I'm going bad. Okay.

D

Right, okay, yeah, but again these are static structures, so.

C

Yeah but anyways I'm I've got a test. I've got to test my model and I might just use that this this one.

D

A great study and what is the stability of these structures versus time? Even Dynamic, instability of the components.

D

All done by simulation.

C

C

Yeah, because some of the structures are obviously not not stable, and you can see that from my my drawing up here that somebody did and they they say that some of this is not stable and in particular over here.

C

So this is a week and we will adherence Junction and over here um microtubules fast growth, growth and fast breakdown.

A

C

So they're they're, showing in stability in these diagrams.

D

Because I think it's true also of acting, though.

C

um Yeah, well they letters like this yeah there's they're, saying that okay.

D

There's another question: now: are you familiar with treadmilling? Yes, okay, good, because that's another thing that can affect things: yeah, a microfilament or I I excuse me, microfilament or microtubule can grow at one end and Shrink at the other end, so yeah.

C

It looks like it moves, but it's not it just yeah drinking and growing yeah.

D

And that could also have an effect here.

C

Yeah there's the two there's I have a paper on the those two uh effects of uh shrinking and growing and.

D

um Yeah, but not in intense segregation context.

D

C

Well, uh they they're what keeps the they think that the cell membrane is kept, um the the the shape of it is kept by the actin filaments growing towards it. Oh okay,.

C

He does this from my course okay. um Well, if I can find the chords.

C

Tidy up um what is the name of the course.

C

Yeah I think this is it um cell biology and the cytoskeleton um with active matter matter. Oh, let's see.

C

Sorry I'm not prepared for this I'll hear you.

C

Sound biology, the cytoskeleton and cell cycle- and it's adapts- they have a okay, oh yeah, that's interesting because it's they're, the instructor actually is doing the research on on this. Okay.

D

Let me ask this question on this picture here: they're, claiming that the that you get a week or unstable adherence Junction, far away from the ECM.

D

Yes, is there any way to estimate the force of pulling it apart?.

C

um I, don't know they pull on tissue and see if it breaks down I'm, not sure.

D

I, don't know you might want to look in it and see. If has anyone well in this configuration, it would seem that there might be a net pull of two components of the adhere instruction, so it pulls apart. Well,.

C

There's there's not very much um acting there.

D

It goes down to the actin stress fiber, some of them.

C

Yeah there's some some there yeah.

D

So what I'm, at least in the in the cartoon yeah that's what I'm saying is there might be, it might be a tug of war.

C

There was one called tug of war yeah.

D

And and uh and you can actually, if you could guesstimate that Force, you could compare the guesstimate with if somebody's done it the actual force, it takes to pull a junction apart.

C

Yeah I'm sure there's some literature that might be yeah well again. I need to do a forward search on this, so this is just very rough. This presentation.

D

Pretty comprehensive.

B

D

C

I want to be covered, but it's like not finished.

B

um I just wanted to go to that last slide for a minute uh before we end the one with the whole cell intercell tensegrity structure, yeah that one so cells are often truncated, isohedrons space, packing design, and then this honeycomb shape for the epithelial tissue. Okay, yeah.

C

If I could spell this right.

B

uh I mean I wonder if different tissues have different sort of optimal packings um just because of the way that the cells are shaped and the function it'd be kind of interesting to see. Yeah.

D

Let me ask you another, a simple, geometric question: uh are you familiar with the the close packing of spheres in three dimensions.

C

Are you asking me you set me up anyway.

D

Just just spheres.

B

I've heard of them yeah.

D

Okay, there are actually two different closed packing Arrangements, oh okay, uh if you look at different layers, how the layers are stacked can be different depending on the screen. Oh yeah, it can be different in three dimensions, so you get two two entirely different kinds of three-dimensional: packets.

C

um Do you know what they're called no.

D

I, don't remember that look up, look up, three-dimensional, packings or something I mean yeah, you know include imagines. Is there it's Unique, but if you've stacked the two Dimensions to get your three dimensions, there are two different ways of stacking them. In fact, it might even be three different ways because there might be a random way of stacking. It.

C

Well, certainly, though, Square cell, in the middle of all of the other cells, and it would change configuration of no.

D

No, no! No! No! No! No I'm! Talking about perfect backings.

C

Oh okay, well, I'm sure that in a living cell or a living tissue.

D

Okay, I'm, just just throwing it out as a consideration: okay,.

C

C

Thank you for helping me with my presentation.

D

Give me giving you more work.

C

um You think you're bad I get um my professor yelling in my ear, when I've done something silly. No, you just ask hard questions: okay, okay, um I'll, stop, sharing them.

D

Yeah, okay, because I think there's also a random way of doing it. Okay, okay.

C

Well, I'll check out the path.

D

Yeah I think there may be a random way of stacking some different layers.

C

D

They still end up with the same. You know, every every sphere has what 12 spheres around it.

B

D

They're, perfect, okay, so these different packings all retain that property.

D

C

Yeah I'm I'm, hoping that I'm just doing epithelial tissue right now, probably.

D

Not yeah two-dimensional, okay, yeah.

D

We have to worry about three dimensions: yeah.