ITBio | Chris Aldrich

📗 Started reading The Vital Question: Energy, Evolution, and the Origins of Complex Life by Nick Lane

📗 Started reading pages 1-18 Introduction: Why is Life the Way it is in The Vital Question: Energy, Evolution, and the Origins of Complex Life by Nick Lane

A quick, but interesting peek into where he intends to go. He lays out some quick background here in the opening. He’s generally a very lucid writer so far. Can’t wait to get in further.

Some may feel like some of the terminology is a hurdle in the opening, so I hope he circles around to define some of his terms a bit better for the audience I suspect he’s trying to reach.

🔖 Upcoming Special Issue “Information Theory in Neuroscience” | Entropy (MDPI)

Bookmarked Special Issue "Information Theory in Neuroscience" (Entropy | MDPI)

As the ultimate information processing device, the brain naturally lends itself to be studied with information theory. Application of information theory to neuroscience has spurred the development of principled theories of brain function, has led to advances in the study of consciousness, and to the development of analytical techniques to crack the neural code, that is to unveil the language used by neurons to encode and process information. In particular, advances in experimental techniques enabling precise recording and manipulation of neural activity on a large scale now enable for the first time the precise formulation and the quantitative test of hypotheses about how the brain encodes and transmits across areas the information used for specific functions. This Special Issue emphasizes contributions on novel approaches in neuroscience using information theory, and on the development of new information theoretic results inspired by problems in neuroscience. Research work at the interface of neuroscience, Information Theory and other disciplines is also welcome. A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory". Deadline for manuscript submissions: 1 December 2017

🔖 Self-Organized Resonance during Search of a Diverse Chemical Space

Bookmarked Self-Organized Resonance during Search of a Diverse Chemical Space (Physical Review Letters)

ABSTRACT Recent studies of active matter have stimulated interest in the driven self-assembly of complex structures. Phenomenological modeling of particular examples has yielded insight, but general thermodynamic principles unifying the rich diversity of behaviors observed have been elusive. Here, we study the stochastic search of a toy chemical space by a collection of reacting Brownian particles subject to periodic forcing. We observe the emergence of an adaptive resonance in the system matched to the drive frequency, and show that the increased work absorption by these resonant structures is key to their stabilization. Our findings are consistent with a recently proposed thermodynamic mechanism for far-from-equilibrium self-organization.

Suggested by First Support for a Physics Theory of Life in Quanta Magazine.

🔖 Spontaneous fine-tuning to environment in many-species chemical reaction networks | PNAS

Bookmarked Spontaneous fine-tuning to environment in many-species chemical reaction networks (Proceedings of the National Academy of Sciences)

Significance A qualitatively more diverse range of possible behaviors emerge in many-particle systems once external drives are allowed to push the system far from equilibrium; nonetheless, general thermodynamic principles governing nonequilibrium pattern formation and self-assembly have remained elusive, despite intense interest from researchers across disciplines. Here, we use the example of a randomly wired driven chemical reaction network to identify a key thermodynamic feature of a complex, driven system that characterizes the “specialness” of its dynamical attractor behavior. We show that the network’s fixed points are biased toward the extremization of external forcing, causing them to become kinetically stabilized in rare corners of chemical space that are either atypically weakly or strongly coupled to external environmental drives. Abstract A chemical mixture that continually absorbs work from its environment may exhibit steady-state chemical concentrations that deviate from their equilibrium values. Such behavior is particularly interesting in a scenario where the environmental work sources are relatively difficult to access, so that only the proper orchestration of many distinct catalytic actors can power the dissipative flux required to maintain a stable, far-from-equilibrium steady state. In this article, we study the dynamics of an in silico chemical network with random connectivity in an environment that makes strong thermodynamic forcing available only to rare combinations of chemical concentrations. We find that the long-time dynamics of such systems are biased toward states that exhibit a fine-tuned extremization of environmental forcing.

Suggested by First Support for a Physics Theory of Life in Quanta Magazine.

👓 First Support for a Physics Theory of Life | Quanta Magazine

Read First Support for a Physics Theory of Life by Natalie Wolchover (Quanta Magazine)

Take chemistry, add energy, get life. The first tests of Jeremy England’s provocative origin-of-life hypothesis are in, and they appear to show how order can arise from nothing.

Interesting article with some great references I’ll need to delve into and read.

The situation changed in the late 1990s, when the physicists Gavin Crooks and Chris Jarzynski derived “fluctuation theorems” that can be used to quantify how much more often certain physical processes happen than reverse processes. These theorems allow researchers to study how systems evolve — even far from equilibrium.

I want to take a look at these papers as well as several about which the article is directly about.

Any claims that it has to do with biology or the origins of life, he added, are “pure and shameless speculations.”

Some truly harsh words from his former supervisor? Wow!

maybe there’s more that you can get for free

Most of what’s here in this article (and likely in the underlying papers) sounds to me to have been heavily influenced by the writings of W. Loewenstein and S. Kauffman. They’ve laid out some models/ideas that need more rigorous testing and work, and this seems like a reasonable start to the process. The “get for free” phrase itself is very S. Kauffman in my mind. I’m curious how many times it appears in his work?

Apparently there’s a beta Bioinformatics group on Stack Exchange now. It’s just come out of alpha in the last few days and it appears anyone can join now.

📺 A Universal Theory of Life: Math, Art & Information by Sara Walker

Watched A Universal Theory of Life: Math, Art & Information from TEDxASU

Dr. Walker introduces the concept of information, then proposes that information may be a necessity for biological complexity in this thought-provoking talk on the origins of life. Sara is a theoretical physicist and astrobiologist, researching the origins and nature of life. She is particularly interested in addressing the question of whether or not “other laws of physics” might govern life, as first posed by Erwin Schrodinger in his famous book What is life?. She is currently an Assistant Professor in the School of Earth and Space Exploration and Beyond Center for Fundamental Concepts in Science at Arizona State University. She is also Fellow of the ASU -Santa Fe Institute Center for Biosocial Complex Systems, Founder of the astrobiology-themed social website SAGANet.org, and is a member of the Board of Directors of Blue Marble Space. She is active in public engagement in science, with recent appearances on “Through the Wormhole” and NPR’s Science Friday.

https://www.youtube.com/watch?v=kXnt79JhrbY

Admittedly, she only had a few short minutes, but it would have been nice if she’d started out with a precise definition of information. I suspect the majority of her audience didn’t know the definition with which she’s working and it would have helped focus the talk.

Her description of Speigelman’s Monster was relatively interesting and not very often seen in much of the literature that covers these areas.

I wouldn’t rate this very highly as a TED Talk as it wasn’t as condensed and simplistic as most, nor was it as hyper-focused, but then again condensing this area into 11 minutes is far from simple task. I do love that she’s excited enough about the topic that she almost sounds a little out of breath towards the end.

There’s an excellent Eddington quote I’ve mentioned before that would have been apropos to have opened up her presentation that might have brought things into higher relief given her talk title:

Suppose that we were asked to arrange the following in two categories–

distance, mass, electric force, entropy, beauty, melody.

I think there are the strongest grounds for placing entropy alongside beauty and melody and not with the first three.

Sir Arthur Stanley Eddington, OM, FRS (1882-1944), a British astronomer, physicist, and mathematician
in The Nature of the Physical World, 1927

🔖 Can entropy be defined for and the Second Law applied to the entire universe? by Arieh Ben-Naim | Arxiv

Bookmarked Can entropy be defined for and the Second Law applied to the entire universe? (arXiv)

This article provides answers to the two questions posed in the title. It is argued that, contrary to many statements made in the literature, neither entropy, nor the Second Law may be used for the entire universe. The origin of this misuse of entropy and the second law may be traced back to Clausius himself. More resent (erroneous) justification is also discussed.

🔖 The hidden simplicity of biology by Paul C W Davies and Sara Imari Walker | Reports on Progress in Physics

Bookmarked The hidden simplicity of biology (Reports on Progress in Physics)

Life is so remarkable, and so unlike any other physical system, that it is tempting to attribute special factors to it. Physics is founded on the assumption that universal laws and principles underlie all natural phenomena, but is it far from clear that there are 'laws of life' with serious descriptive or predictive power analogous to the laws of physics. Nor is there (yet) a 'theoretical biology' in the same sense as theoretical physics. Part of the obstacle in developing a universal theory of biological organization concerns the daunting complexity of living organisms. However, many attempts have been made to glimpse simplicity lurking within this complexity, and to capture this simplicity mathematically. In this paper we review a promising new line of inquiry to bring coherence and order to the realm of biology by focusing on 'information' as a unifying concept.

Downloadable free copy available on ResearchGate.

🔖 "Opposite-of"-information improves similarity calculations in phenotype ontologies

Bookmarked "Opposite-of"-information improves similarity calculations in phenotype ontologies (bioRxiv)

One of the most important use cases of ontologies is the calculation of similarity scores between a query and items annotated with classes of an ontology. The hierarchical structure of an ontology does not necessarily reflect all relevant aspects of the domain it is modelling, and this can reduce the performance of ontology-based search algorithms. For instance, the classes of phenotype ontologies may be arranged according to anatomical criteria, but individual phenotypic features may affect anatomic entities in opposite ways. Thus, "opposite" classes may be located in close proximity in an ontology; for example enlarged liver and small liver are grouped under abnormal liver size. Using standard similarity measures, these would be scored as being similar, despite in fact being opposites. In this paper, we use information about opposite ontology classes to extend two large phenotype ontologies, the human and the mammalian phenotype ontology. We also show that this information can be used to improve rankings based on similarity measures that incorporate this information. In particular, cosine similarity based measures show large improvements. We hypothesize this is due to the natural embedding of opposite phenotypes in vector space. We support the idea that the expressivity of semantic web technologies should be explored more extensively in biomedical ontologies and that similarity measures should be extended to incorporate more than the pure graph structure defined by the subclass or part-of relationships of the underlying ontologies.

👓 A Conversation with @LPachter (BS ’94) | Caltech

Read A Conversation with Lior Pachter (BS '94) (The California Institute of Technology)

Pachter, a computational biologist, returns to CalTech to study the role and function of RNA.

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

Bookmarked The Epidemic Spreading Model and the Direction of Information Flow in Brain Networks (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.

IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease, March 1-3

Bookmarked 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))