The Society for Mathematical Biology - e-Conference 2020.
August 17 - 20, 2020
Tag: ITBio
To recognize strange extraterrestrial life and solve biological mysteries on this planet, scientists are searching for an objective definition for life’s basic units.
I’ve been following a bit of David’s work, but obviously there’s some newer material I need to catch up on. I like the general philosophical thrust of their direction here. I can see some useful abstractions to higher math here, maybe an analogy to a “calculus of biology” which doesn’t look at single points, but rates of change of that point(s).
Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.
ITBio – Chris Aldrich (feed) ❧
Hey, wait! He’s not only following me, but a very distinct subset of my posts! 🙂
This is the first time I’ve ever seen someone indicate that they’ve done this in the wild.
I’ll also admit that this is a really great looking blogroll too! I’m going to have to mine it for the bunch of feeds that I’m not already following.
The first comprehensive general resource on state-of-the-art protocell research, describing current approaches to making new forms of life from scratch in the laboratory.
Protocells offers a comprehensive resource on current attempts to create simple forms of life from scratch in the laboratory. These minimal versions of cells, known as protocells, are entities with lifelike properties created from nonliving materials, and the book provides in-depth investigations of processes at the interface between nonliving and living matter. Chapters by experts in the field put this state-of-the-art research in the context of theory, laboratory work, and computer simulations on the components and properties of protocells. The book also provides perspectives on research in related areas and such broader societal issues as commercial applications and ethical considerations. The book covers all major scientific approaches to creating minimal life, both in the laboratory and in simulation. It emphasizes the bottom-up view of physicists, chemists, and material scientists but also includes the molecular biologists' top-down approach and the origin-of-life perspective. The capacity to engineer living technology could have an enormous socioeconomic impact and could bring both good and ill. Protocells promises to be the essential reference for research on bottom-up assembly of life and living technology for years to come. It is written to be both resource and inspiration for scientists working in this exciting and important field and a definitive text for the interested layman.
SUM is a dazzling exploration of funny and unexpected afterlives that have never been considered -- each presented as a vignette that offers us a stunning lens through which to see ourselves here and now.
In one afterlife you may find that God is the size of a microbe and is unaware of your existence. In another, your creators are a species of dim-witted creatures who built us to figure out what they could not. In a different version of the afterlife you work as a background character in other people's dreams. Or you may find that God is a married couple struggling with discontent, or that the afterlife contains only those people whom you remember, or that the hereafter includes the thousands of previous gods who no longer attract followers. In some afterlives you are split into your different ages; in some you are forced to live with annoying versions of yourself that represent what you could have been; in others you are re-created from your credit card records and Internet history. David Eagleman proposes many versions of our purpose here; we are mobile robots for cosmic mapmakers, we are reunions for a scattered confederacy of atoms, we are experimental subjects for gods trying to understand what makes couples stick together.
These wonderfully imagined tales -- at once funny, wistful, and unsettling -- are rooted in science and romance and awe at our mysterious existence: a mixture of death, hope, computers, immortality, love, biology, and desire that exposes radiant new facets of our humanity.
Looks interesting, but I’ll pass at the moment. Ran across reference on a philosophical level in a Complexity related talk from the Santa Fe Institute.
President and William H. Miller Professor of Complex Systems
For all of you waiting with bated breath! ALife 2020 Keynote Speaker Announcement #2: Melanie Mitchell @MelMitchell1 Who is a living legend in Complex Systems & has a brand new book out, "Artificial Intelligence: A Guide for Thinking Humans." http://vermontcomplexsystems.org/events/ALIFE-2020/ #Alife2020 pic.twitter.com/WIRQbuuZ2G
— ALIFE Conference 2020 (@ALifeConf) December 3, 2019
👓 What can Schrödinger’s cat say about 3D printers on Mars? | Aeon | Aeon Essays
A cat is alive, a sofa is not: that much we know. But a sofa is also part of life. Information theory tells us why
A nice little essay in my area, but I’m not sure there’s anything new in it for me. It is nice that they’re trying to break some of the problem down into smaller components before building it back up into something else. Reframing things can always be helpful. Here, in particular, they’re reframing the definitions of life and alive.
Ed Yong (The Atlantic)Decades of early research on the genetics of depression were built on nonexistent foundations. How did that happen?
📑 Solomon Golomb (1932–2016) | Stephen Wolfram Blog
As it happens, he’d already done some work on coding theory—in the area of biology. The digital nature of DNA had been discovered by Jim Watson and Francis Crick in 1953, but it wasn’t yet clear just how sequences of the four possible base pairs encoded the 20 amino acids. In 1956, Max Delbrück—Jim Watson’s former postdoc advisor at Caltech—asked around at JPL if anyone could figure it out. Sol and two colleagues analyzed an idea of Francis Crick’s and came up with “comma-free codes” in which overlapping triples of base pairs could encode amino acids. The analysis showed that exactly 20 amino acids could be encoded this way. It seemed like an amazing explanation of what was seen—but unfortunately it isn’t how biology actually works (biology uses a more straightforward encoding, where some of the 64 possible triples just don’t represent anything). ❧
I recall talking to Sol about this very thing when I sat in on a course he taught at USC on combinatorics. He gave me his paper on it and a few related issues as I was very interested at the time about the applications of information theory and biology.
I’m glad I managed to sit in on the class and still have the audio recordings and notes. While I can’t say that Newton taught me calculus, I can say I learned combinatorics from Golomb.
👓 Solomon Golomb (1932–2016) | Stephen Wolfram
Stephen Wolfram (blog.stephenwolfram.com)The Most-Used Mathematical Algorithm Idea in History
An octillion. A billion billion billion. That’s a fairly conservative estimate of the number of times a cellphone or other device somewhere in the world has generated a bit using a maximum-length linear-feedback shift register sequence. It’s probably the single most-used mathematical algorithm idea in history. And the main originator of this idea was Solomon Golomb, who died on May 1—and whom I knew for 35 years.
Solomon Golomb’s classic book Shift Register Sequences, published in 1967—based on his work in the 1950s—went out of print long ago. But its content lives on in pretty much every modern communications system. Read the specifications for 3G, LTE, Wi-Fi, Bluetooth, or for that matter GPS, and you’ll find mentions of polynomials that determine the shift register sequences these systems use to encode the data they send. Solomon Golomb is the person who figured out how to construct all these polynomials.
A fantastic and pretty comprehensive obit for Sol. He did miss out on more of Sol’s youth as well as his cross-town chess rivalry with Basil Gordon when they both lived in Baltimore, but before they lived across town from each other again in Los Angeles.
Many of the fantastical seeming stories here, as well as Sol’s personality read very true to me with respect to the man I knew for almost two decades.
🔖 [1903.07456] Self-Organization and Artificial Life | arXiv
Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions.
Background
Organisms live and die by the amount of information they acquire about their environment. The systems analysis of complex metabolic networks allows us to ask how such information translates into fitness. A metabolic network transforms nutrients into biomass. The better it uses information on available nutrient availability, the faster it will allow a cell to divide.
Results
I here use metabolic flux balance analysis to show that the accuracy I (in bits) with which a yeast cell can sense a limiting nutrient's availability relates logarithmically to fitness as indicated by biomass yield and cell division rate. For microbes like yeast, natural selection can resolve fitness differences of genetic variants smaller than 10-6, meaning that cells would need to estimate nutrient concentrations to very high accuracy (greater than 22 bits) to ensure optimal growth. I argue that such accuracies are not achievable in practice. Natural selection may thus face fundamental limitations in maximizing the information processing capacity of cells.
Conclusion
The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective.
https://doi.org/10.1186/1752-0509-1-33
Received: 01 March 2007 Accepted: 30 July 2007 Published: 30 July 2007
Hat tip to Paul Davies in The Demon in the Machine
Self-replication is a capacity common to every species of living thing, and simple physical intuition dictates that such a process must invariably be fueled by the production of entropy. Here, we undertake to make this intuition rigorous and quantitative by deriving a lower bound for the amount of heat that is produced during a process of self-replication in a system coupled to a thermal bath. We find that the minimum value for the physically allowed rate of heat production is determined by the growth rate, internal entropy, and durability of the replicator, and we discuss the implications of this finding for bacterial cell division, as well as for the pre-biotic emergence of self-replicating nucleic acids.
https://doi.org/10.1063/1.4818538
Syndicated copy also available on arXiv: https://arxiv.org/abs/1209.1179
Hat tip to Paul Davies in The Demon in the Machine