16w5113: Stochastic and Deterministic Models for Evolutionary Biology | Banff International Research Station

Bookmarked Stochastic and Deterministic Models for Evolutionary Biology (Banff International Research Station)
A BIRS / Casa Matemática Oaxaca Workshop arriving in Oaxaca, Mexico Sunday, July 31 and departing Friday August 5, 2016

Evolutionary biology is a rapidly changing field, confronted to many societal problems of increasing importance: impact of global changes, emerging epidemics, antibiotic resistant bacteria… As a consequence, a number of new problematics have appeared over the last decade, challenging the existing mathematical models. There exists thus a demand in the biology community for new mathematical models allowing a qualitative or quantitative description of complex evolution problems. In particular, in the societal problems mentioned above, evolution is often interacting with phenomena of a different nature: interaction with other organisms, spatial dynamics, age structure, invasion processes, time/space heterogeneous environment… The development of mathematical models able to deal with those complex interactions is an ambitious task. Evolutionary biology is interested in the evolution of species. This process is a combination of several phenomena, some occurring at the individual level (e.g. mutations), others at the level of the entire population (competition for resources), often consisting of a very large number of individuals. the presence of very different scales is indeed at the core of theoretical evolutionary biology, and at the origin of many of the difficulties that biologists are facing. The development of new mathematical models thus requires a joint work of three different communities of researchers: specialists of partial differential equations, specialists of probability theory, and theoretical biologists. The goal of this workshop is to gather researchers from each of these communities, currently working on close problematics. Those communities have usually few interactions, and this meeting would give them the opportunity to discuss and work around a few biological thematics that are especially challenging mathematically, and play a crucial role for biological applications.

The role of a spatial structure in models for evolution: The introduction of a spatial structure in evolutionary biology models is often challenging. It is however well known that local adaptation is frequent in nature: field data show that the phenotypes of a given species change considerably across its range. The spatial dynamics of a population can also have a deep impact on its evolution. Assessing e.g. the impact of global changes on species requires the development of robust mathematical models for spatially structured populations.

The first type of models used by theoretical biologists for this type of problems are IBM (Individual Based Models), which describe the evolution of a finite number of individuals, characterized by their position and a phenotype. The mathematical analysis of IBM in spatially homogeneous situations has provided several methods that have been successful in the theoretical biology community (see the theory of Adaptive Dynamics). On the contrary, very few results exist so far on the qualitative properties of such models for spatially structured populations.

The second class of mathematical approach for this type of problem is based on ”infinite dimensional” reaction-diffusion: the population is structured by a continuous phenotypic trait, that affects its ability to disperse (diffusion), or to reproduce (reaction). This type of model can be obtained as a large population limit of IBM. The main difficulty of these models (in the simpler case of asexual populations) is the term modeling the competition from resources, that appears as a non local competition term. This term prevents the use of classical reaction diffusion tools such as the comparison principle and sliding methods. Recently, promising progress has been made, based on tools from elliptic equations and/or Hamilton-Jacobi equations. The effects of small populations can however not be observed on such models. The extension of these models and methods to include these effects will be discussed during the workshop.

Eco-evolution models for sexual populations:An essential question already stated by Darwin and Fisher and which stays for the moment without answer (although it continues to intrigue the evolutionary biologists) is: ”Why does sexual reproduction maintain?” Indeed this reproduction way is very costly since it implies a large number of gametes, the mating and the choice of a compatible partner. During the meiosis phasis, half of the genetical information is lost. Moreover, the males have to be fed and during the sexual mating, individual are easy preys for predators. A partial answer is that recombination plays a main role by better eliminating the deleterious mutations and by increasing the diversity. Nevertheless, this theory is not completely satisfying and many researches are devoted to understanding evolution of sexual populations and comparison between asexual and sexual reproduction. Several models exist to model the influence of sexual reproduction on evolving species. The difficulty compared to asexual populations is that a detailed description of the genetic basis of phenotypes is required, and in particular include recombinations. For sexual populations, recombination plays a main role and it is essential to understand. All models require strong biological simplifications, the development of relevant mathematical methods for such mechanisms then requires a joint work of mathematicians and biologists. This workshop will be an opportunity to set up such collaborations.

The first type of model considers a small number of diploid loci (typically one locus and two alleles), while the rest of the genome is considered as fixed. One can then define the fitness of every combination of alleles. While allowing the modeling of specific sexual effects (such as dominant/recessive alleles), this approach neglects the rest of the genome (and it is known that phenotypes are typically influenced by a large number of loci). An opposite approach is to consider a large number of loci, each locus having a small and additive impact on the considered phenotype. This approach then neglects many microscopic phenomena (epistasis, dominant/recessive alleles…), but allows the derivation of a deterministic model, called the infinitesimal model, in the case of a large population. The construction of a good mathematical framework for intermediate situation would be an important step forward.

The evolution of recombination and sex is very sensitive to the interaction between several evolutionary forces (selection, migration, genetic drift…). Modeling these interactions is particularly challenging and our understanding of the recombination evolution is often limited by strong assumptions regarding demography, the relative strength of these different evolutionary forces, the lack of spatial structure… The development of a more general theoretical framework based on new mathematical developments would be particularly valuable.

Another problem, that has received little attention so far and is worth addressing, is the modeling of the genetic material exchanges in asexual population. This phenomena is frequent in micro-organisms : horizontal gene transfers in bacteria, reassortment or recombination in viruses. These phenomena share some features with sexual reproduction. It would be interesting to see if the effect of this phenomena can be seen as a perturbation of existing asexual models. This would in particular be interesting in spatially structured populations (e.g. viral epidemics), since the the mathematical analysis of spatially structured asexual populations is improving rapidly.

Modeling in evolutionary epidemiology: Mathematical epidemiology has been developing since more than a century ago. Yet, the integration of population genetics phenomena to epidemiology is relatively recent. Microbial pathogens (bacteria and viruses) are particularly interesting organisms because their short generation times and large mutation rates allow them to adapt relatively fast to changing environments. As a consequence, ecological (demography) and evolutionary (population genetics) processes often occur at the same pace. This raises many interesting problems.

A first challenge is the modeling of the spatial dynamics of an epidemics. The parasites can evolve during the epidemics of a new host population, either to adapt to a heterogeneous environment, or because it will itself modify the environment as it invades. The applications of such studies are numerous: antibiotic management, agriculture… An aspect of this problem for which our workshop can bring a significant contribution (thanks to the diversity of its participants) is the evolution of the pathogen diversity. During the large expansion produced by an epidemics, there is a loss of diversity in the invading parasites, since most pathogens originate from a few parents. The development of mathematical models for those phenomena is challenging: only a small number of pathogens are present ahead of the epidemic front, while the number of parasites rapidly become very large after the infection. The interaction between a stochastic micro scale and a deterministic macro scale is apparent here, and deserves a rigorous mathematical analysis.

Another interesting phenomena is the effect of a sudden change of the environment on a population of pathogens. Examples of such situations are for instance the antibiotic treatment of an infected patients, or the transmission of a parasite to a new host species (transmission of the avian influenza to human beings, for instance). Related experiments are relatively easy to perform, and called evolutionary rescue experiments. So far, this question has received limited attention from the mathematical community. The key is to estimate the probability that a mutant well adapted to the new environment existed in the original population, or will appear soon after the environmental change. Interactions between biologists specialists of those questions and mathematicians should lead to new mathematical problems.

Nick Lane and Philip Ball Discuss Mitochondria, Sex, and How to Live Longer

Bookmarked Nick Lane and Philip Ball Discuss Mitochondria, Sex, and How to Live Longer by Philip Ball (Nautil.us)
In his 2010 book, Life Ascending: The Ten Great Inventions of Evolution, Nick Lane, a biochemist at University College London, explores with eloquence and clarity the big questions of life: how it began, why we age and die, and why we have sex. Lane been steadily constructing an alternative view of evolution to the one in which genes explain it all. He argues that some of the major events during evolutionary history, including the origin of life itself, are best understood by considering where the energy comes from and how it is used. Lane describes these ideas in his 2015 book, The Vital Question: Why Is Life the Way It Is?. Recently Bill Gates called it “an amazing inquiry into the origins of life,” adding, Lane “is one of those original thinkers who make you say: More people should know about this guy’s work.” Nautilus caught up with Lane in his laboratory in London and asked him about his ideas on aging, sex, and death.
Biochemist Nick Lane explains the elements of life, sex, and aging in an engaging popular science interview.

Read more

Books by Nick Lane

A new view of the tree of life

Bookmarked A new view of the tree of life (Nature Microbiology)
An update to the €˜tree of life has revealed a dominance of bacterial diversity in many ecosystems and extensive evolution in some branches of the tree. It also highlights how few organisms we have been able to cultivate for further investigation.

Abstract

The tree of life is one of the most important organizing principles in biology. Gene surveys suggest the existence of an enormous number of branches, but even an approximation of the full scale of the tree has remained elusive. Recent depictions of the tree of life have focused either on the nature of deep evolutionary relationships or on the known, well-classified diversity of life with an emphasis on eukaryotes. These approaches overlook the dramatic change in our understanding of life’s diversity resulting from genomic sampling of previously unexamined environments. New methods to generate genome sequences illuminate the identity of organisms and their metabolic capacities, placing them in community and ecosystem contexts. Here, we use new genomic data from over 1,000 uncultivated and little known organisms, together with published sequences, to infer a dramatically expanded version of the tree of life, with Bacteria, Archaea and Eukarya included. The depiction is both a global overview and a snapshot of the diversity within each major lineage. The results reveal the dominance of bacterial diversification and underline the importance of organisms lacking isolated representatives, with substantial evolution concentrated in a major radiation of such organisms. This tree highlights major lineages currently underrepresented in biogeochemical models and identifies radiations that are probably important for future evolutionary analyses.

Laura A. Hug, Brett J. Baker, Karthik Anantharaman, Christopher T. Brown, Alexander J. Probst, Cindy J. Castelle, Cristina N. Butterfield, Alex W. Hernsdorf, Yuki Amano, Kotaro Ise, Yohey Suzuki, Natasha Dudek, David A. Relman, Kari M. Finstad, Ronald Amundson, Brian C. Thomas & Jillian F. Banfield in Nature Microbiology, Article number: 16048 (2016) doi:10.1038/nmicrobiol.2016.48

 

A reformatted view of the tree in Fig. 1in which each major lineage represents the same amount of evolutionary distance.
A reformatted view of the tree in Fig. 1in which each major lineage represents the same amount of evolutionary distance.

Carl Zimmer also has a nice little write up of the paper in today’s New York Times:

Carl Zimmer
in Scientists Unveil New ‘Tree of Life’ from The New York Times 4/11/16

 

Forthcoming ITBio-related book from Sean Carroll: “The Big Picture: On the Origins of Life, Meaning, and the Universe Itself”

In catching up on blogs/reading from the holidays, I’ve noticed that physicist Sean Carroll has a forthcoming book entitled The Big Picture: On the Origins of Life, Meaning, and the Universe Itself (Dutton, May 10, 2016) that will be of interest to many of our readers. One can already pre-order the book via Amazon.

Prior to the holidays Sean wrote a blogpost that contains a full overview table of contents, which will give everyone a stronger idea of its contents. For convenience I’ll excerpt it below.

I’ll post a review as soon as a copy arrives, but it looks like a strong new entry in the category of popular science books on information theory, biology and complexity as well as potentially the areas of evolution, the origin of life, and physics in general.

As a side bonus, for those reading this today (1/15/16), I’ll note that Carroll’s 12 part lecture series from The Great Courses The Higgs Boson and Beyond (The Learning Company, February 2015) is 80% off.

The Big Picture

 

THE BIG PICTURE: ON THE ORIGINS OF LIFE, MEANING, AND THE UNIVERSE ITSELF

0. Prologue

* Part One: Cosmos

  • 1. The Fundamental Nature of Reality
  • 2. Poetic Naturalism
  • 3. The World Moves By Itself
  • 4. What Determines What Will Happen Next?
  • 5. Reasons Why
  • 6. Our Universe
  • 7. Time’s Arrow
  • 8. Memories and Causes

* Part Two: Understanding

  • 9. Learning About the World
  • 10. Updating Our Knowledge
  • 11. Is It Okay to Doubt Everything?
  • 12. Reality Emerges
  • 13. What Exists, and What Is Illusion?
  • 14. Planets of Belief
  • 15. Accepting Uncertainty
  • 16. What Can We Know About the Universe Without Looking at It?
  • 17. Who Am I?
  • 18. Abducting God

* Part Three: Essence

  • 19. How Much We Know
  • 20. The Quantum Realm
  • 21. Interpreting Quantum Mechanics
  • 22. The Core Theory
  • 23. The Stuff of Which We Are Made
  • 24. The Effective Theory of the Everyday World
  • 25. Why Does the Universe Exist?
  • 26. Body and Soul
  • 27. Death Is the End

* Part Four: Complexity

  • 28. The Universe in a Cup of Coffee
  • 29. Light and Life
  • 30. Funneling Energy
  • 31. Spontaneous Organization
  • 32. The Origin and Purpose of Life
  • 33. Evolution’s Bootstraps
  • 34. Searching Through the Landscape
  • 35. Emergent Purpose
  • 36. Are We the Point?

* Part Five: Thinking

  • 37. Crawling Into Consciousness
  • 38. The Babbling Brain
  • 39. What Thinks?
  • 40. The Hard Problem
  • 41. Zombies and Stories
  • 42. Are Photons Conscious?
  • 43. What Acts on What?
  • 44. Freedom to Choose

* Part Six: Caring

  • 45. Three Billion Heartbeats
  • 46. What Is and What Ought to Be
  • 47. Rules and Consequences
  • 48. Constructing Goodness
  • 49. Listening to the World
  • 50. Existential Therapy
  • Appendix: The Equation Underlying You and Me
  • Acknowledgments
  • Further Reading
  • References
  • Index

Source: Sean Carroll | The Big Picture: Table of Contents

Can computers help us read the mind of nature? by Paul Davies | The Guardian

For too long, scientists focused on what we can see. Now they are at last starting to decode life’s software.

“A soup of chemicals may spontaneously form a reaction network, but what does it take for such a molecular muddle to begin coherently organising information flow and storage? Rather than looking to biology or chemistry, we can perhaps dream that advances in the mathematics of information theory hold the key.”

Paul Davies, physicist, writer, and broadcaster
in Can computers help us read the mind of nature? in The Guardian

 

 ‘When we look at a plant or an animal we see the physical forms, not the swirling patterns of instructions inside them.’ Photograph: Abir Sultan/EPA
‘When we look at a plant or an animal we see the physical forms, not the swirling patterns of instructions inside them.’ Photograph: Abir Sultan/EPA

Breaking the code | The Economist

Bookmarked Breaking the code (The Economist)
Brief book overview of Matthew Cobb's "Life’s Greatest Secret" from The Economist.
For those interested in some of the history behind genetics, evolution, biology and information theory, the following book, which I just saw the attached review in The Economist, is likely to be of interest:

Life’s Greatest Secret: The Story of the Race to Crack the Genetic Code. By Matthew Cobb. Basic Books; 434 pages; $29.99. Profile Books; £25.

In 1953 James Watson and Francis Crick, with the help of Rosalind Franklin and Maurice Wilkins, described the structure of the molecule at the heart of life. Deoxyribonucleic acid, better known as DNA, was, they said, a double helix, two spirals joined across the middle by pairs of four chemical bases, like a twisted ladder. That work earned Messrs Crick, Watson and Wilkins a Nobel prize and a place in the history books. The image of the double helix now often stands for biology, or even science, itself.

But this was merely the most visible breakthrough in a long struggle to understand the engine of life—how traits are inherited, mutated and weeded out by natural selection, and how the whole mysterious process works at the biochemical level. It is that lesser-known history that Matthew Cobb, a professor of animal behaviour at the University of Manchester, aims to sketch in his book, which has been shortlisted for the Royal Society’s Winton prize for science writing.

The result is a fascinating reminder of just how hard-won are the seemingly obvious facts of modern biology. The development of genetics was a tale of confusion, accident, frustration and the occasional flash of insight. It was, says Dr Cobb, as important as the Manhattan or Apollo projects, but with no government support and little money, carried out by scientists interested in the question for its own sake.

The researchers started from almost total ignorance. William Harvey, better known for describing the circulation of the blood, wondered in the 17th century what could explain why children’s skin colour was often a blend of their parents’, whereas they share a sex with only one, and can have an eye colour different from either.

In the late 19th century a monk, Gregor Mendel, established, through experiments on pea plants, the basic rules of inherited traits. A Danish biologist, Wilhelm Johannsen, coined the term “gene” in 1909 to describe whatever it was that Mendel had found. But as late as 1933 scientists were still debating whether genes were physical things or just useful abstractions, and how they could transmit traits. Scientists knew that DNA existed, but many considered it a boring bit of scaffolding in the cell. Proteins, which come in zillions of different varieties, were seen by many as the only things exciting enough to account for all the diversity seen in life.

After the second world war, ideas from information theory, arising out of wartime work on computers and automation, percolated into biology. Once the structure of DNA had been established, those ideas helped crack the problem of how the four chemical bases do their job. Proteins are built by stringing together 20 different sorts of amino acid. Strings of three bases within a DNA molecule represent these amino acids, but with 64 such triplets, there is much redundancy which information theory alone could not fully explain. Years of painstaking lab-work were needed to reconcile theory with reality.

Dr Cobb is good on the human side of the story, showing science as fuelled by rivalry, jealousy, competitiveness and wonder. The only downside is that he must marshal hundreds of scientists across several disciplines into around 300 pages of narrative. The results can sometimes be dense, and readers without a command of biological jargon will frequently find themselves consulting the glossary for guidance. But the cracking of the code of life is a great story, of which this is an accomplished telling.

Source: Breaking the code | The Economist, 

 

Life’s Greatest Secret

The Math That Connects Pluto to DNA — NOVA Next | PBS

Bookmarked The Math That Connects Pluto to DNA by Alex RileyAlex Riley (NOVA Next | PBS)
How a mathematical breakthrough from the 1960s now powers everything from spacecraft to cell phones.
Concurrent with the recent Pluto fly by, Alex Riley has a great popular science article on PBS that helps put the application of information theory and biology into perspective for the common person. Like a science version of “The Princess Bride”, this story has a little bit of everything that could be good and entertaining: information theory, biology, DNA, Reed-Solomon codes, fossils, interplanetary exploration, mathematics, music, genetics, computers, and even paleontology. Fans of Big History are sure to love the interconnections presented here.

Reed-Solomon codes correct for common transmission errors, including missing pixels (white), false signals (black), and paused transmissions (the white stripe).
Reed-Solomon codes correct for common transmission errors, including missing pixels (white), false signals (black), and paused transmissions (the white stripe).

Microscopic view of glass DNA storage beads

Game Theory’s Tit-for-Tat is Just a Mathematically Complete Version of Religion’s Golden Rule

Francis Fukuyama (1952- ), American political scientist, political economist, author
in The Origins of Political Order: From Prehuman Times to the French Revolution (Farrar, Straus and Giroux, 2011)

 

Why Information Grows: The Evolution of Order, from Atoms to Economies

I just ordered a copy of Why Information Grows: The Evolution of Order, from Atoms to Economies by Cesar Hidalgo. Although it seems more focused on economics, the base theory seems to fit right into some similar thoughts I’ve long held about biology.

Why Information Grows: The Evolutiion of Order from Atoms to Economies by Cesar Hidalgo
Why Information Grows: The Evolutiion of Order from Atoms to Economies by Cesar Hidalgo

 

From the book description:

“What is economic growth? And why, historically, has it occurred in only a few places? Previous efforts to answer these questions have focused on institutions, geography, finances, and psychology. But according to MIT’s antidisciplinarian César Hidalgo, understanding the nature of economic growth demands transcending the social sciences and including the natural sciences of information, networks, and complexity. To understand the growth of economies, Hidalgo argues, we first need to understand the growth of order.

At first glance, the universe seems hostile to order. Thermodynamics dictates that over time, order–or information–will disappear. Whispers vanish in the wind just like the beauty of swirling cigarette smoke collapses into disorderly clouds. But thermodynamics also has loopholes that promote the growth of information in pockets. Our cities are pockets where information grows, but they are not all the same. For every Silicon Valley, Tokyo, and Paris, there are dozens of places with economies that accomplish little more than pulling rocks off the ground. So, why does the US economy outstrip Brazil’s, and Brazil’s that of Chad? Why did the technology corridor along Boston’s Route 128 languish while Silicon Valley blossomed? In each case, the key is how people, firms, and the networks they form make use of information.

Seen from Hidalgo’s vantage, economies become distributed computers, made of networks of people, and the problem of economic development becomes the problem of making these computers more powerful. By uncovering the mechanisms that enable the growth of information in nature and society, Why Information Grows lays bear the origins of physical order and economic growth. Situated at the nexus of information theory, physics, sociology, and economics, this book propounds a new theory of how economies can do, not just more, but more interesting things.”

Nicolas Perony: Puppies! Now that I’ve got your attention, complexity theory | TED

For those who are looking for a good, simple, and entertaining explanation of the concept of emergent properties and behavior within complexity theory (or Big History), I just came across a nice TED talk that simplifies complexity using a few animal examples including a cute puppy video as well as a bat and a meerkat example. The latter two also have implications for evolution and survival which are lovely examples as well.

[ted id=1916]

Richard Dawkins Interview: This Is My Vision Of “Life” | Edge.org

Bookmarked This Is My Vision Of "Life" by John Brockman (edge.org)
The Edge.org's interview with Richard Dawkins.
Richard Dawkins [4.30.15]

“My vision of life is that everything extends from replicators, which are in practice DNA molecules on this planet. The replicators reach out into the world to influence their own probability of being passed on. Mostly they don’t reach further than the individual body in which they sit, but that’s a matter of practice, not a matter of principle. The individual organism can be defined as that set of phenotypic products which have a single route of exit of the genes into the future. That’s not true of the cuckoo/reed warbler case, but it is true of ordinary animal bodies. So the organism, the individual organism, is a deeply salient unit. It’s a unit of selection in the sense that I call a “vehicle”.  There are two kinds of unit of selection. The difference is a semantic one. They’re both units of selection, but one is the replicator, and what it does is get itself copied. So more and more copies of itself go into the world. The other kind of unit is the vehicle. It doesn’t get itself copied. What it does is work to copy the replicators which have come down to it through the generations, and which it’s going to pass on to future generations. So we have this individual replicator dichotomy. They’re both units of selection, but in different senses. It’s important to understand that they are different senses.”

Richard Dawkins
Richard Dawkins

RICHARD DAWKINS is an evolutionary biologist; Emeritus Charles Simonyi Professor of the Public Understanding of Science, Oxford; Author, The Selfish Gene; The Extended Phenotype; Climbing Mount Improbable; The God Delusion; An Appetite For Wonder; and (forthcoming) A Brief Candle In The Dark.

Watch the entire video interview and read the transcript at Edge.org.

NIMBioS Workshop: Information Theory and Entropy in Biological Systems

Over the next few days, I’ll be maintaining a Storify story covering information related to and coming out of the Information Theory and Entropy Workshop being sponsored by NIMBios at the Unviersity of Tennessee, Knoxville.

For those in attendance or participating by watching the live streaming video (or even watching the video after-the-fact), please feel free to use the official hashtag #entropyWS, and I’ll do my best to include your tweets, posts, and material into the story stream for future reference.

For journal articles and papers mentioned in/at the workshop, I encourage everyone to join the Mendeley.com group ITBio: Information Theory, Microbiology, Evolution, and Complexity and add them to the group’s list of papers. Think of it as a collaborative online journal club of sorts.

Those participating in the workshop are also encouraged to take a look at a growing collection of researchers and materials I maintain here. If you have materials or resources you’d like to contribute to the list, please send me an email or include them via the suggestions/submission form or include them in the comments section below.

Resources for Information Theory and Biology

RSS Icon  RSS Feed for BoffoSocko posts tagged with #ITBio

 

Probability Models for DNA Sequence Evolution

Bookmarked Probability Models for DNA Sequence Evolution (Springer, 2008, 2nd Edition) by Rick Durrett (math.duke.edu)
While browsing through some textbooks and researchers today, I came across a fantastic looking title: Probability Models for DNA Sequence Evolution by Rick Durrett (Springer, 2008). While searching his website at Duke, I noticed that he’s made a .pdf copy of a LaTeX version of the 2nd edition available for download.   I hope others find it as interesting and useful as I do.

I’ll also give him a shout out for being a mathematician with a fledgling blog: Rick’s Ramblings.

Book Cover of Probability Models for DNA Sequence Evolution by Richard Durrett
Probability Models for DNA Sequence Evolution by Richard Durrett

Announcement: Special issue of Entropy: “Information Theoretic Incentives for Cognitive Systems”

Editor’s Note: Dr. Christoph Salge asked me to cross-post the following notice from the Entropy site here. I’m sure many of the readers of the blog will find the topic to be of interest.


 

Logo for the journal Entropy

 

Dear Colleagues,

In recent years, ideas such as “life is information processing” or “information holds the key to understanding life” have become more common. However, how can information, or more formally Information Theory, increase our understanding of life, or life-like systems?

Information Theory not only has a profound mathematical basis, but also typically provides an intuitive understanding of processes, such as learning, behavior and evolution terms of information processing.

In this special issue, we are interested in both:

  1. the information-theoretic formalization and quantification of different aspects of life, such as driving forces of learning and behavior generation, information flows between neurons, swarm members and social agents, and information theoretic aspects of evolution and adaptation, and
  2. the simulation and creation of life-like systems with previously identified principles and incentives.

Topics with relation to artificial and natural systems:

  • information theoretic intrinsic motivations
  • information theoretic quantification of behavior
  • information theoretic guidance of artificial evolution
  • information theoretic guidance of self-organization
  • information theoretic driving forces behind learning
  • information theoretic driving forces behind behavior
  • information theory in swarms
  • information theory in social behavior
  • information theory in evolution
  • information theory in the brain
  • information theory in system-environment distinction
  • information theory in the perception action loop
  • information theoretic definitions of life

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs).

Deadline for manuscript submissions: 28 February 2015

Special Issue Editors

Guest Editor
Dr. Christoph Salge
Adaptive Systems Research Group,University of Hertfordshire, College Lane, AL10 9AB Hatfield, UK
Website: http://homepages.stca.herts.ac.uk/~cs08abi
E-Mail: c.salge@herts.ac.uk
Phone: +44 1707 28 4490
Interests: Intrinsic Motivation (Empowerment); Self-Organization; Guided Self-Organization; Information-Theoretic Incentives for Social Interaction; Information-Theoretic Incentives for Swarms; Information Theory and Computer Game AI

Guest Editor
Dr. Georg Martius
Cognition and Neurosciences, Max Planck Institute for Mathematics in the Sciences Inselstrasse 22, 04103 Leipzig, Germany
Website: http://www.mis.mpg.de/jjost/members/georg-martius.html
E-Mail: martius@mis.mpg.de
Phone: +49 341 9959 545
Interests: Autonomous Robots; Self-Organization; Guided Self-Organization; Information Theory; Dynamical Systems; Machine Learning; Neuroscience of Learning; Optimal Control

Guest Editor
Dr. Keyan Ghazi-Zahedi
Information Theory of Cognitive Systems, Max Planck Institute for Mathematics in the Sciences Inselstrasse 22, 04103 Leipzig, Germany
Website: http://personal-homepages.mis.mpg.de/zahedi
E-Mail: zahedi@mis.mpg.de
Phone: +49 341 9959 535
Interests: Embodied Artificial Intelligence; Information Theory of the Sensorimotor Loop; Dynamical Systems; Cybernetics; Self-organisation; Synaptic plasticity; Evolutionary Robotics

Guest Editor
Dr. Daniel Polani
Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Website: http://homepages.feis.herts.ac.uk/~comqdp1/
E-Mail: d.polani@herts.ac.uk
Interests: artificial intelligence; artificial life; information theory for intelligent information processing; sensor Evolution; collective and multiagent systems

Information Theory and Paleoanthropology

A few weeks ago I had communicated a bit with paleoanthropologist John Hawks.  I wanted to take a moment to highlight the fact that he maintains an excellent blog primarily concerning his areas of research which include anthropology, genetics and evolution.  Even more specifically, he is one of the few people in these areas with at least a passing interest in the topic of information theory as it relates to his work. I recommend everyone take a look at his information theory specific posts.

silhouette of John Hawks from his blog

I’ve previously written a brief review of John Hawks’ (in collaboration with Anthony Martin) “Major Transitions in Evolution” course from The Learning Company as part of their Great Courses series of lectures. Given my interest in the MOOC revolution in higher education, I’ll also mention that Dr. Hawks has recently begun a free Coursera class entitled “Human Evolution: Past and Future“. I’m sure his current course focuses more on the area of human evolution compared with the prior course which only dedicated a short segment on this time period.  Given Hawks’ excellent prior teaching work, I’m sure this will be of general interest to readers interested in information theory as it relates to evolution, biology, and big history.

I’d love to hear from others in the area of anthropology who are interested in information theoretical applications.