To recognize strange extraterrestrial life and solve biological mysteries on this planet, scientists are searching for an objective definition for life’s basic units.
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.
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.
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
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
Decades of early research on the genetics of depression were built on nonexistent foundations. How did that happen?
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.
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.
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.
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.
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.
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.
The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective.
Received: 01 March 2007 Accepted: 30 July 2007 Published: 30 July 2007
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.
So far there’s nothing new for me here. He’s encapsulating a lot of prior books I’ve read. (Though he’s doing an incredible job of it.) There are a handful of references that I’ll want to go take a look at though.