🔖 The Epidemic Spreading Model and the Direction of Information Flow in Brain Networks

The Epidemic Spreading Model and the Direction of Information Flow in Brain Networks by J. Meier, X. Zhou, A. Hillebrand, P. Tewarie, C.J. Stam, P. Van Mieghem (NeuroImage, February 5, 2017)
The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process.
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IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease, March 1-3

IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease (Institute for Pure and Applied Mathematics, UCLA | March 1-3, 2017)
Epigenetics refers to information transmitted during cell division other than the DNA sequence per se, and it is the language that distinguishes stem cells from somatic cells, one organ from another, and even identical twins from each other. In contrast to the DNA sequence, the epigenome is relatively susceptible to modification by the environment as well as stochastic perturbations over time, adding to phenotypic diversity in the population. Despite its strong ties to the environment, epigenetics has never been well reconciled to evolutionary thinking, and in fact there is now strong evidence against the transmission of so-called “epi-alleles,” i.e. epigenetic modifications that pass through the germline.

However, genetic variants that regulate stochastic fluctuation of gene expression and phenotypes in the offspring appear to be transmitted as an epigenetic or even Lamarckian trait. Furthermore, even the normal process of cellular differentiation from a single cell to a complex organism is not understood well from a mathematical point of view. There is increasingly strong evidence that stem cells are highly heterogeneous and in fact stochasticity is necessary for pluripotency. This process appears to be tightly regulated through the epigenome in development. Moreover, in these biological contexts, “stochasticity” is hardly synonymous with “noise”, which often refers to variation which obscures a “true signal” (e.g., measurement error) or which is structural, as in physics (e.g., quantum noise). In contrast, “stochastic regulation” refers to purposeful, programmed variation; the fluctuations are random but there is no true signal to mask.

This workshop will serve as a forum for scientists and engineers with an interest in computational biology to explore the role of stochasticity in regulation, development and evolution, and its epigenetic basis. Just as thinking about stochasticity was transformative in physics and in some areas of biology, it promises to fundamentally transform modern genetics and help to explain phase transitions such as differentiation and cancer.

This workshop will include a poster session; a request for poster titles will be sent to registered participants in advance of the workshop.

Speaker List:
Adam Arkin (Lawrence Berkeley Laboratory)
Gábor Balázsi (SUNY Stony Brook)
Domitilla Del Vecchio (Massachusetts Institute of Technology)
Michael Elowitz (California Institute of Technology)
Andrew Feinberg (Johns Hopkins University)
Don Geman (Johns Hopkins University)
Anita Göndör (Karolinska Institutet)
John Goutsias (Johns Hopkins University)
Garrett Jenkinson (Johns Hopkins University)
Andre Levchenko (Yale University)
Olgica Milenkovic (University of Illinois)
Johan Paulsson (Harvard University)
Leor Weinberger (University of California, San Francisco (UCSF))

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Entropy | Special Issue: Maximum Entropy and Bayesian Methods

Entropy | Special Issue : Maximum Entropy and Bayesian Methods (mdpi.com)
Open for submission now
Deadline for manuscript submissions: 31 August 2017
A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 31 August 2017

Special Issue Editor


Guest Editor

Dr. Brendon J. Brewer

 

Department of Statistics, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
Website | E-MailPhone: +64275001336
Interests: bayesian inference, markov chain monte carlo, nested sampling, MaxEnt

Special Issue Information

Dear Colleagues,

Whereas Bayesian inference has now achieved mainstream acceptance and is widely used throughout the sciences, associated ideas such as the principle of maximum entropy (implicit in the work of Gibbs, and developed further by Ed Jaynes and others) have not. There are strong arguments that the principle (and variations, such as maximum relative entropy) is of fundamental importance, but the literature also contains many misguided attempts at applying it, leading to much confusion.

This Special Issue will focus on Bayesian inference and MaxEnt. Some open questions that spring to mind are: Which proposed ways of using entropy (and its maximisation) in inference are legitimate, which are not, and why? Where can we obtain constraints on probability assignments, the input needed by the MaxEnt procedure?

More generally, papers exploring any interesting connections between probabilistic inference and information theory will be considered. Papers presenting high quality applications, or discussing computational methods in these areas, are also welcome.

Dr. Brendon J. Brewer
Guest Editor

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 1500 CHF (Swiss Francs).

No papers have been published in this special issue yet.

Source: Entropy | Special Issue : Maximum Entropy and Bayesian Methods

🔖 The Hypercycle: A Principle of Natural Self-Organization | Springer

The Hypercycle - A Principle of Natural Self-Organization | M. Eigen | Springer by Manfred Eigen and Peter Schuster (Springer, 1979)
This book originated from a series of papers which were published in "Die Naturwissenschaften" in 1977178. Its division into three parts is the reflection of a logic structure, which may be abstracted in the form of three theses:

A. Hypercycles are a principle of natural self-organization allowing an inte­gration and coherent evolution of a set of functionally coupled self-rep­licative entities.

B. Hypercycles are a novel class of nonlinear reaction networks with unique properties, amenable to a unified mathematical treatment.

C. Hypercycles are able to originate in the mutant distribution of a single Darwinian quasi-species through stabilization of its diverging mutant genes. Once nucleated hypercycles evolve to higher complexity by a process analogous to gene duplication and specialization. In order to outline the meaning of the first statement we may refer to another principle of material self organization, namely to Darwin's principle of natural selection. This principle as we see it today represents the only understood means for creating information, be it the blue print for a complex living organism which evolved from less complex ancestral forms, or be it a meaningful sequence of letters the selection of which can be simulated by evolutionary model games.

Part A in .pdf format.

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🔖 Cognition and biology: perspectives from information theory

Cognition and biology: perspectives from information theory by Roderick Wallace (ncbi.nlm.nih.gov)
The intimate relation between biology and cognition can be formally examined through statistical models constrained by the asymptotic limit theorems of communication theory, augmented by methods from statistical mechanics and nonequilibrium thermodynamics. Cognition, often involving submodules that act as information sources, is ubiquitous across the living state. Less metabolic free energy is consumed by permitting crosstalk between biological information sources than by isolating them, leading to evolutionary exaptations that assemble shifting, tunable cognitive arrays at multiple scales, and levels of organization to meet dynamic patterns of threat and opportunity. Cognition is thus necessary for life, but it is not sufficient: An organism represents a highly patterned outcome of path-dependent, blind, variation, selection, interaction, and chance extinction in the context of an adequate flow of free energy and an environment fit for development. Complex, interacting cognitive processes within an organism both record and instantiate those evolutionary and developmental trajectories.
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🔖 Thermodynamics of Prediction

Thermodynamics of Prediction by Susanne Still, David A. Sivak, Anthony J. Bell, and Gavin E. Crooks (journals.aps.org Phys. Rev. Lett. 109, 120604 (2012))
A system responding to a stochastic driving signal can be interpreted as computing, by means of its dynamics, an implicit model of the environmental variables. The system’s state retains information about past environmental fluctuations, and a fraction of this information is predictive of future ones. The remaining nonpredictive information reflects model complexity that does not improve predictive power, and thus represents the ineffectiveness of the model. We expose the fundamental equivalence between this model inefficiency and thermodynamic inefficiency, measured by dissipation. Our results hold arbitrarily far from thermodynamic equilibrium and are applicable to a wide range of systems, including biomolecular machines. They highlight a profound connection between the effective use of information and efficient thermodynamic operation: any system constructed to keep memory about its environment and to operate with maximal energetic efficiency has to be predictive.
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🔖 Statistical Physics of Adaptation

Statistical Physics of Adaptation by Nikolay Perunov, Robert A. Marsland, and Jeremy L. England (journals.aps.org Phys. Rev. X 6, 021036 (2016))
Whether by virtue of being prepared in a slowly relaxing, high-free energy initial condition, or because they are constantly dissipating energy absorbed from a strong external drive, many systems subject to thermal fluctuations are not expected to behave in the way they would at thermal equilibrium. Rather, the probability of finding such a system in a given microscopic arrangement may deviate strongly from the Boltzmann distribution, raising the question of whether thermodynamics still has anything to tell us about which arrangements are the most likely to be observed. In this work, we build on past results governing nonequilibrium thermodynamics and define a generalized Helmholtz free energy that exactly delineates the various factors that quantitatively contribute to the relative probabilities of different outcomes in far-from-equilibrium stochastic dynamics. By applying this expression to the analysis of two examples—namely, a particle hopping in an oscillating energy landscape and a population composed of two types of exponentially growing self-replicators—we illustrate a simple relationship between outcome-likelihood and dissipative history. In closing, we discuss the possible relevance of such a thermodynamic principle for our understanding of self-organization in complex systems, paying particular attention to a possible analogy to the way evolutionary adaptations emerge in living things.
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🔖 Meaning = Information + Evolution by Carlo Rovelli

Meaning = Information + Evolution by Carlo Rovelli (arxiv.org)
Notions like meaning, signal, intentionality, are difficult to relate to a physical word. I study a purely physical definition of "meaningful information", from which these notions can be derived. It is inspired by a model recently illustrated by Kolchinsky and Wolpert, and improves on Dretske classic work on the relation between knowledge and information. I discuss what makes a physical process into a "signal".
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🔖 Irreversibility and Heat Generation in the Computing Process by R. Landauer

Irreversibility and Heat Generation in the Computing Process by R. Landauer (ieeexplore.ieee.org)
It is argued that computing machines inevitably involve devices which perform logical functions that do not have a single-valued inverse. This logical irreversibility is associated with physical irreversibility and requires a minimal heat generation, per machine cycle, typically of the order of kT for each irreversible function. This dissipation serves the purpose of standardizing signals and making them independent of their exact logical history. Two simple, but representative, models of bistable devices are subjected to a more detailed analysis of switching kinetics to yield the relationship between speed and energy dissipation, and to estimate the effects of errors induced by thermal fluctuations.

A classical paper in the history of entropy.

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🔖 Energy flow and the organization of life | Complexity

Energy flow and the organization of life by Harold Morowitz and Eric Smith (Complexity, September 2007)
Understanding the emergence and robustness of life requires accounting for both chemical specificity and statistical generality. We argue that the reverse of a common observation—that life requires a source of free energy to persist—provides an appropriate principle to understand the emergence, organization, and persistence of life on earth. Life, and in particular core biochemistry, has many properties of a relaxation channel that was driven into existence by free energy stresses from the earth's geochemistry. Like lightning or convective storms, the carbon, nitrogen, and phosphorus fluxes through core anabolic pathways make sense as the order parameters in a phase transition from an abiotic to a living state of the geosphere. Interpreting core pathways as order parameters would both explain their stability over billions of years, and perhaps predict the uniqueness of specific optimal chemical pathways.

Download .pdf copy

[1]
H. Morowitz and E. Smith, “Energy flow and the organization of life,” Complexity, vol. 13, no. 1. Wiley-Blackwell, pp. 51–59, 2007 [Online]. Available: http://dx.doi.org/10.1002/cplx.20191
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🔖 Evidence for a limit to human lifespan | Nature Research

Evidence for a limit to human lifespan by Xiao Dong, Brandon Milholland, and Jan Vijg (nature.com)
Driven by technological progress, human life expectancy has increased greatly since the nineteenth century. Demographic evidence has revealed an ongoing reduction in old-age mortality and a rise of the maximum age at death, which may gradually extend human longevity. Together with observations that lifespan in various animal species is flexible and can be increased by genetic or pharmaceutical intervention, these results have led to suggestions that longevity may not be subject to strict, species-specific genetic constraints. Here, by analysing global demographic data, we show that improvements in survival with age tend to decline after age 100, and that the age at death of the world’s oldest person has not increased since the 1990s. Our results strongly suggest that the maximum lifespan of humans is fixed and subject to natural constraints.
[1]
X. Dong, B. Milholland, and J. Vijg, “Evidence for a limit to human lifespan.,” Nature, vol. 538, no. 7624, pp. 257–259, Oct. 2016. [PubMed]
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🔖 Hayflick, his limit, and cellular ageing | Nature Reviews Molecular Cell Biology

Hayflick, his limit, and cellular ageing by Jerry W. Shay and Woodring E. Wright ( Nature Reviews Molecular Cell Biology)
Almost 40 years ago, Leonard Hayflick discovered that cultured normal human cells have limited capacity to divide, after which they become senescent — a phenomenon now known as the ‘Hayflick limit’. Hayflick's findings were strongly challenged at the time, and continue to be questioned in a few circles, but his achievements have enabled others to make considerable progress towards understanding and manipulating the molecular mechanisms of ageing.
[1]
J. Shay and W. Wright, “Hayflick, his limit, and cellular ageing.,” Nat Rev Mol Cell Biol, vol. 1, no. 1, pp. 72–6, Oct. 2000. [PubMed]
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🔖 Thermodynamic Uncertainty Relation for Biomolecular Processes, Phys. Rev. Lett. 114, 158101 (2015)

Thermodynamic Uncertainty Relation for Biomolecular Processes by Andre C. Barato and Udo Seifert (Phys. Rev. Lett. 114, 158101 (2015) - journals.aps.org)
Biomolecular systems like molecular motors or pumps, transcription and translation machinery, and other enzymatic reactions, can be described as Markov processes on a suitable network. We show quite generally that, in a steady state, the dispersion of observables, like the number of consumed or produced molecules or the number of steps of a motor, is constrained by the thermodynamic cost of generating it. An uncertainty ε requires at least a cost of 2k_B T/ε^2 independent of the time required to generate the output.
[1]
A. C. Barato and U. Seifert, “Thermodynamic Uncertainty Relation for Biomolecular Processes,” Physical Review Letters, vol. 114, no. 15. American Physical Society (APS), 15-Apr-2015 [Online]. Available: http://dx.doi.org/10.1103/PhysRevLett.114.158101 [Source]
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🔖 Causal Entropic Forces, Phys. Rev. Lett. 110, 168702 (2013)

Causal Entropic Forces by A. D. Wissner-Gross and C. E. Freer (Phys. Rev. Lett. 110, 168702 (2013) journals.aps.org )
Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.
[1]
A. D. Wissner-Gross and C. E. Freer, “Causal Entropic Forces,” Physical Review Letters, vol. 110, no. 16. American Physical Society (APS), 19-Apr-2013 [Online]. Available: http://dx.doi.org/10.1103/PhysRevLett.110.168702 [Source]

 

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🔖 How Life (and Death) Spring From Disorder | Quanta Magazine

How Life (and Death) Spring From Disorder by Philip Ball (Quanta Magazine)
Life was long thought to obey its own set of rules. But as simple systems show signs of lifelike behavior, scientists are arguing about whether this apparent complexity is all a consequence of thermodynamics.

This is a nice little general interest article by Philip Ball that does a relatively good job of covering several of my favorite topics (information theory, biology, complexity) for the layperson. While it stays relatively basic, it links to a handful of really great references, many of which I’ve already read, though several appear to be new to me. [1][2][3][4][5][6][7][8][9][10]

While Ball has a broad area of interests and coverage in his work, he’s certainly one of the best journalists working in this subarea of interests today. I highly recommend his work to those who find this area interesting.

References

[1]
E. Mayr, What Makes Biology Unique? Cambridge University Press, 2004.
[2]
A. Wissner-Gross and C. Freer, “Causal entropic forces.,” Phys Rev Lett, vol. 110, no. 16, p. 168702, Apr. 2013. [PubMed]
[3]
A. Barato and U. Seifert, “Thermodynamic uncertainty relation for biomolecular processes.,” Phys Rev Lett, vol. 114, no. 15, p. 158101, Apr. 2015. [PubMed]
[4]
J. Shay and W. Wright, “Hayflick, his limit, and cellular ageing.,” Nat Rev Mol Cell Biol, vol. 1, no. 1, pp. 72–6, Oct. 2000. [PubMed]
[5]
X. Dong, B. Milholland, and J. Vijg, “Evidence for a limit to human lifespan,” Nature, vol. 538, no. 7624. Springer Nature, pp. 257–259, 05-Oct-2016 [Online]. Available: http://dx.doi.org/10.1038/nature19793
[6]
H. Morowitz and E. Smith, “Energy Flow and the Organization of Life,” Santa Fe Institute, 07-Aug-2006. [Online]. Available: http://samoa.santafe.edu/media/workingpapers/06-08-029.pdf. [Accessed: 03-Feb-2017]
[7]
R. Landauer, “Irreversibility and Heat Generation in the Computing Process,” IBM Journal of Research and Development, vol. 5, no. 3. IBM, pp. 183–191, Jul-1961 [Online]. Available: http://dx.doi.org/10.1147/rd.53.0183
[8]
C. Rovelli, “Meaning = Information + Evolution,” arXiv, Nov. 2006 [Online]. Available: https://arxiv.org/abs/1611.02420
[9]
N. Perunov, R. A. Marsland, and J. L. England, “Statistical Physics of Adaptation,” Physical Review X, vol. 6, no. 2. American Physical Society (APS), 16-Jun-2016 [Online]. Available: http://dx.doi.org/10.1103/PhysRevX.6.021036 [Source]
[10]
S. Still, D. A. Sivak, A. J. Bell, and G. E. Crooks, “Thermodynamics of Prediction,” Physical Review Letters, vol. 109, no. 12. American Physical Society (APS), 19-Sep-2012 [Online]. Available: http://dx.doi.org/10.1103/PhysRevLett.109.120604 [Source]
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