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Do brain cells need to be connected to have meaning?

"Roy is among a number of scientists working in the fields of neuroscience and artificial intelligence (AI) who suspect that the brain may not be as connected as distributed representation suggests. The basis of their alternative model, called localist representation, is that a single neuron can represent a dog, a cat, or any other object or concept. These neurons can be considered symbols since they have meaning on a stand-alone basis. However, as Roy explains, this doesn't necessarily mean only one neuron represents a dog; such "concept cells" are high-level neurons, which fire in response to the firing of an assortment of low-level neurons that represent the legs, ears, body, tail, etc."

Colbert Sesanker's insight:

An alternative to the neurons being pushed around as symbols is that the individual neuron is actually processing some of the information in the object it responds to.

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Life Science Paradigms and Foundations
Aims to enumerate novel life science paradigms and theories. All views presented here are completely my own and do not reflect those of anyone else.  Don't take anything here too seriously yet. It's just brainstorming.
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Coexisting Stable Equilibria in a Multiple-allele Population Genetics Model

In this paper we find and classify all patterns for a single locus three- and four-allele population genetics models in continuous time. A pattern for a k-allele model means all coexisting locally stable equilibria with respect to the flow defined by the equations p˙i=pi(ri−r),i=1,...,k, where pi,ri are the frequency and marginal fitness of allele Ai, respectively, and r is the mean fitness of the population. It is well known that for the two-allele model there are only three patterns depending on the relative fitness between the homozygotes and the heterozygote. It turns out that for the three-allele model there are 14 patterns and for the four-allele model there are 117 patterns. With the help of computer simulations, we find 2351 patterns for the five-allele model. For the six-allele model, there are more than 60,000 patterns. In addition, for each pattern of the three-allele model, we also determine the asymptotic behavior of solutions of the above system of equations as t→∞. The problem of finding patterns has been studied in the past and it is an important problem because the results can be used to predict the long-term genetic makeup of a population.
Colbert Sesanker's insight:

Three player game with all coexisting strategies enumerated (not necessarily Nash Equilibria). Population frequencies governed by Replicator Equation. Higher player games considered  using simulations (up to n = 6).

 

This came out of my undergraduate senior thesis. Our claim that this is an 'evolutionary model'  is very false.

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A theory of biological relativity: no privileged level of causation

A theory of biological relativity: no privileged level of causation | Life Science Paradigms and Foundations | Scoop.it
Must higher level biological processes always be derivable from lower level data and mechanisms, as assumed by the idea that an organism is completely defined by its genome? Or are higher level properties necessarily also causes of lower level behaviour, involving actions and interactions both ways? This article uses modelling of the heart, and its experimental basis, to show that downward causation is necessary and that this form of causation can be represented as the influences of initial and boundary conditions on the solutions of the differential equations used to represent the lower level processes. These insights are then generalized. A priori, there is no privileged level of causation. The relations between this form of ‘biological relativity’ and forms of relativity in physics are discussed. Biological relativity can be seen as an extension of the relativity principle by avoiding the assumption that there is a privileged scale at which biological functions are determined.
Colbert Sesanker's insight:

Highlight:

" This article uses modelling of the heart, and its experimental basis, to show that downward causation is necessary and that this form of causation can be represented as the influences of initial and boundary conditions on the solutions of the differential equations used to represent the lower level processes.." 

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Statistical physics of self-replication (The Journal of Chemical Physics)

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.
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A New Physics Theory of Life (Quanta Magazine)

A New Physics Theory of Life (Quanta Magazine) | Life Science Paradigms and Foundations | Scoop.it
An MIT physicist has proposed the provocative idea that life exists because the law of increasing entropy drives matter to acquire lifelike physical properties.
Colbert Sesanker's insight:

Getting back to the outline in "What is Life?". 
More of an * announcement * that the Schrodinger perspective on "What is Life" is reviving.  

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PNAS : Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

"Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Hao Ye et al (2015), Proceedings of the National Academy of Sciences: http://dx.doi.org/10.1073/pnas.1417063112"

Colbert Sesanker's insight:

this one  explains the concept of a 'strange attractor '  then goes on the explain taken's theorem and attractor reconstruction. It's not stated explicitly, but this methodology is useful only if the periodic orbit is chaotic.

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Donald E. Ingber : Wyss Institute at Harvard

Donald E. Ingber : Wyss Institute at Harvard | Life Science Paradigms and Foundations | Scoop.it
Donald E. Ingber is the Founding Director of the Wyss Institute for Biologically Inspired Engineering at Harvard University, the Judah Folkman Professor of Vascular Biology at Harvard Medical School and Boston Children's Hospital, and Professor of Bioengineering at the Harvard School of Engineering and Applied Sciences. He received his B.A., M.A., M.Phil., M.D. and Ph.D. from Yale University. Dr. Ingber is a founder of the emerging field of biologically inspired engineering, and at the Wyss Institute, he oversees a multifaceted effort to identify the mechanisms that living organisms use to self-assemble from molecules and cells, and to apply these design principles to develop advanced materials and devices for healthcare and to improve sustainability. He also leads the Biomimetic Microsystems platform in which microfabrication techniques from the computer industry are used to build functional circuits with living cells as components. His most recent innovation is a technology for building tiny, complex, three-dimensional models of living human organs, or "Organs on Chips", that mimic complicated human functions as a way to replace traditional animal-based methods for testing of drugs and establishment of human disease models. In addition, Dr. Ingber has made major contributions to mechanobiology, tissue engineering, tumor angiogenesis, systems biology, and nanobiotechnology. He was the first to recognize that tensegrity architecture is a fundamental principle that governs how living cells are structured to respond biochemically to mechanical forces, and to demonstrate that integrin receptors mediate cellular mechanotransduction. Dr. Ingber has authored more than 375 publications and 85 patents, and has received numerous honors including the Holst Medal, Pritzker Award from the Biomedical Engineering Society, Rous-Whipple Award from the American Society for Investigative Pathology, Lifetime Achievement Award from the Society of In Vitro Biology, and the Department of Defense Breast Cancer Innovator Award. He also serves on the Board of Directors of the National Space Biomedical Research Institute, and is a member of both the American Institute for Medical and Biological Engineering, and the Institute of Medicine of the National Academies.
Colbert Sesanker's insight:

The Ingber lab studies the solid-state and tensegrity paradigms of the cell. See below for details of these paradigms. These two paradigms may be related to: 

1. Models of Coherent Excitations (e.g., Frohlich's rate equation)

2. Cytoskeletal structures and the Molecules scaffolded to them. (e.g., Microtubule Network)

3. Fourth Gel phase of water discovered by Pollock. (as applied to solid-state paradigm)

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University of Washington: Pollack Laboratory - Pollack Laboratory

University of Washington: Pollack Laboratory - Pollack Laboratory | Life Science Paradigms and Foundations | Scoop.it

"Water has three phases – gas, liquid, and solid; but recent findings from our laboratory imply the presence of a surprisingly extensive fourth phase that occurs at interfaces. This finding may have unexpectedly profound implication for chemistry, physics and biology."

 

"Water and Cell Biology:
Contemporary views of cell biology consider water merely as a background carrier of the more important molecules of life. However, water may be a central player in life processes."

Colbert Sesanker's insight:

In particular the highly structured 4th phase of water present in the cell can lower the viscous damping of vibrating molecules (e.g., proteins).

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Tensegrity and Complex Systems Biology (Harvard)

Tensegrity and Complex Systems Biology (Harvard) | Life Science Paradigms and Foundations | Scoop.it

"Importantly, working with collaborators, such as Drs. Ning Wang (Dept. of Respiratory Biology, Harvard School of Public Health) and Dimitrije Stamenovic (Dept. of Biomedical Engineering, Boston U.), we have been able to demonstrate that living mammalian cells behave mechanically like tensegrity structures. Moreover, we have developed a theoretical formulation of the tensegrity model starting from first mechanical principles that has yielded accurate qualitative and quantitative predictions of many static and dynamic cell mechanical behaviors. We are currently trying to extend and strengthen this computational approach to explain systems-wide mechanical properties in mammalian cells, and to explore their hierarchical basis

 

 

The cellular tensegrity model proposes that the whole cell is a prestressed tensegrity structure, although geodesic structures are also found in the cell at smaller size scales (e.g. clathrin-coated vesicles, viral capsids). In the model, tensional forces are borne by cytoskeletal microfilaments and intermediate filaments, and these forces are balanced by interconnected structural elements that resist compression, most notably internal microtubule struts and ECM adhesions. However, individual filaments can have dual functions and hence bear either tension or compression in different structural contexts or at different size scales (e.g. contractile microfilaments generate tension, whereas actin microfilament bundles that are rigidified by cross-links bear compression in filopodia). The tensional prestress that stabilizes the whole cell is generated actively by the contractile actomyosin apparatus. Additional passive contributions to this prestress come from cell distension through adhesions to the ECM and other cells, osmotic forces acting on the cell membrane, and forces exerted by filament polymerization. Intermediate filaments that interconnect at many points along microtubules, microfilaments and the nuclear surface provide mechanical stiffness to the cell based on their material properties and on their ability to act as suspensory cables that interconnect and tensionally stiffen the entire cytoskeleton and nuclear lattice."

 

Colbert Sesanker's insight:

Of particular interest is the solid state view of the cell where enzymes and substrates that mediate biochemical reactions are physically immobilized and secured on cytoskeletal scaffolds, the ECM and organelles.

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tensegrity - Ingber, Donald A

tensegrity - Ingber, Donald A | Life Science Paradigms and Foundations | Scoop.it

"Due to his work, the cytoskeleton is now widely believed to be a tensegrity structure, and tensegrity mechanics at the cellular level is now close to replacing all previous mechanical models of the cell."

Colbert Sesanker's insight:

Getting the basics right is a good first start... There is a lot of room to test these ideas computationally. Also, this design principle could inspire new tensegrity designs in engineering, robotics in particular.

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The Histone Code: Judd Rice Laboratory at the USC/Norris Comprehensive Cancer Center

The Histone Code: Judd Rice Laboratory at the USC/Norris Comprehensive Cancer Center | Life Science Paradigms and Foundations | Scoop.it

"Increasing evidence indicates that the post-translationally modified histones serve as extremely selective binding platforms for specific regulatory proteins that drive distinct nuclear processes.

It has been known for over 45 years now that histones can be post-translationally modified by specific enzymes that ‘write?a histone code by adding or removing a number of different chemical modifications, including acetyl, phosphoryl and methyl groups (Figure 2). Since these modifications occur only on specific amino acid residues on specific histones in various eukaryotic organisms, these observations strongly linked the modifications?involvement in nuclear processes. For example, the acetylation of key lysine residues of histone H3 and H4 by enzymes known as histone acetyltransferases (HATs) was known to play a pivotal role in transcriptional activation." 

 

Colbert Sesanker's insight:

Is it a Rube-Goldberg machine or does it resist compression?

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Science: Nutritional Control of Reproductive Status in Honeybees via DNA Methylation

Science: Nutritional Control of Reproductive Status in Honeybees via DNA Methylation | Life Science Paradigms and Foundations | Scoop.it

"Fertile queens and sterile workers are alternative forms of the adult female honeybee that develop from genetically identical larvae following differential feeding with royal jelly. We show that silencing the expression of DNA methyltransferase Dnmt3, a key driver of epigenetic global reprogramming, in newly hatched larvae led to a royal jelly–like effect on the larval developmental trajectory; the majority of Dnmt3 small interfering RNA–treated individuals emerged as queens with fully developed ovaries. Our results suggest that DNA methylation in Apis is used for storing epigenetic information, that the use of that information can be differentially altered by nutritional input, and that the flexibility of epigenetic modifications underpins, profound shifts in developmental fates, with massive implications for reproductive and behavioral status."

Colbert Sesanker's insight:

 The bee colony is genetically identical, but Queens are made when workers feed larva 'Royal Jelly' for an extended period of time. Queens exhibit vastly different gene expression patterns from workers. This is caused by epigenetic modifications. 


The queen differentiation program was replicated by blocking (using small siRNA to silence methyltransferase Dnmt3) CpG methylation in larva.

 

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Paper archive from Stanford CS class (cs379c) that includes: Von Neumann's "Theory of Self-Reproducing Autonoma".

Paper archive from Stanford CS class (cs379c) that includes:  Von Neumann's "Theory of Self-Reproducing Autonoma". | Life Science Paradigms and Foundations | Scoop.it
Colbert Sesanker's insight:

Search 'Neumann1966.pdf'  to get a a copy of the pdf

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Neural Networks and Deep Learning

Neural Networks and Deep Learning | Life Science Paradigms and Foundations | Scoop.it
Neural networks are one of the most beautiful programming paradigms ever invented. In the conventional approach to programming, we tell the computer what to do, breaking big problems up into many small, precisely defined tasks that the computer can easily perform. By contrast, in a neural network we don't tell the computer how to solve our problem. Instead, it learns from observational data, figuring out its own solution to the problem at hand.

Automatically learning from data sounds promising. However, until 2006 we didn't know how to train neural networks to surpass more traditional approaches, except for a few specialized problems. What changed in 2006 was the discovery of techniques for learning in so-called deep neural networks. These techniques are now known as deep learning. They've been developed further, and today deep neural networks and deep learning achieve outstanding performance on many important problems in computer vision, speech recognition, and natural language processing. They're being deployed on a large scale by companies such as Google, Microsoft, and Chinese search giant Baidu.

The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning. After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems. And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.
Colbert Sesanker's insight:

concise and clear introduction to neural networks including deep learning

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Evolutionary game theory: cells as players - (Molecular BioSystems)

Evolutionary game theory: cells as players -  (Molecular BioSystems) | Life Science Paradigms and Foundations | Scoop.it
In two papers we review game theory applications in biology below the level of cognitive living beings. It can be seen that evolution and natural selection replace the rationality of the actors appropriately. Even in these micro worlds, competing situations and cooperative relationships can be found and modeled by evolutionary game theory. Also those units of the lowest levels of life show different strategies for different environmental situations or different partners. We give a wide overview of evolutionary game theory applications to microscopic units. In this first review situations on the cellular level are tackled. In particular metabolic problems are discussed, such as ATP-producing pathways, secretion of public goods and cross-feeding. Further topics are cyclic competition among more than two partners, intra- and inter-cellular signalling, the struggle between pathogens and the immune system, and the interactions of cancer cells. Moreover, we introduce the theoretical basics to encourage scientists to investigate problems in cell biology and molecular biology by evolutionary game theory.
Colbert Sesanker's insight:

Clear and simple introduction to evolutionary game theory. Explains key ideas.

 

Explains:

 

*  Payoff Matrices

*  Nash Equilibria

*  Mixed Strategies

*  Evolutionary Stable Strategies

*  Enumerates all possible Two Player Game Strategies

*  Replicator Equation for dynamics of populations with different strategies  

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A framework for modelling gene regulation which accommodates non-equilibrium mechanisms (BMC Biology)

A framework for modelling gene regulation which accommodates non-equilibrium mechanisms (BMC Biology) | Life Science Paradigms and Foundations | Scoop.it

"A quantitative approach to analysing gene regulation in terms of the interactions between transcription factors (TFs) and DNA was first developed for λ repressor in Escherichia coli [1]. In the eubacterial context, TFs bind and unbind from naked DNA and it was assumed that these processes quickly reach thermodynamic equilibrium. Equilibrium statistical mechanics could then be used to calculate the probability of DNA microstates, or patterns of TF binding to DNA. The gene-regulation function, which expresses the dependence of mRNA transcription rate on the concentrations of the TFs, was then calculated as an average over the microstate probabilities. This equilibrium “thermodynamic formalism” has been widely used to analyse gene regulation in eubacteria [2-6].

Eukaryotic genomes use several mechanisms that dissipate energy. These include epigenetic mechanisms, such as DNA methylation, nucleosome remodelling and post-translational modification and demodification of histones, transcription factors, transcriptional co-regulators and components of the transcriptional machinery, like RNA polymerase or Mediator. In each case, energy is expended to operate the mechanism, through consumption of intermediary metabolites such as ATP. Background metabolic processes maintain the concentration of such metabolites, thereby providing the free energy required away from thermodynamic equilibrium."

 

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Bacteria replicate close to the physical limit of efficiency (Nature)

Bacteria replicate close to the physical limit of efficiency (Nature) | Life Science Paradigms and Foundations | Scoop.it
The common gut bacterium Escherichia coli typically takes about 20 minutes to duplicate itself in good conditions. Could it do it any faster? A little, but not much, says biological physicist Jeremy England at the Massachusetts Institute of Technology in Cambridge. In a preprint1, he estimates that bacteria are impressively close — within a factor of two or three — to the limiting efficiency of replication set by the laws of physics.
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PNAS : Dynamical evidence for causality between galactic cosmic rays and interannual variation

"Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature. Anastasios A. Tsonis et al (2015),  http://dx.doi.org/10.1073/pnas.1420291112"

Colbert Sesanker's insight:

Goes into methodology details on cosmic ray paper below

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Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature (PNAS)

Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature (PNAS) | Life Science Paradigms and Foundations | Scoop.it
Here we use newly available methods to examine the dynamical association between cosmic rays (CR) and global temperature (GT) in the 20th-century observational record. We find no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend; however, on short interannual timescales, we find a significant, although modest, causal effect of CR on short-term, year-to-year variability in GT. Thus, although CR clearly do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales, providing another interesting piece of the puzzle in our understanding of factors influencing climate variability.
Colbert Sesanker's insight:

This along with a few other recent Sugihara papers are focusing on equation free ideas, which circumvent many issues with parametric models. Here the data IS the model and predictions come from the data's compressability, which may be hidden in an aperiodic structure (resulting from chaos)  in the observable's time series. 

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The Fourth Phase of Water: Dr. Gerald Pollack (University of Washington)

Does water have a fourth phase, beyond solid, liquid and vapor?

University of Washington Bioengineering Professor Gerald Pollack answers this question, and intrigues us to consider the implications of this finding. Not all water is H2O, a radical departure from what you may have learned from textbooks.

Pollack received his PhD in biomedical engineering from the University of Pennsylvania in 1968. He then joined the University of Washington faculty and is now professor of Bioengineering. His interests have ranged broadly, from biological motion and cell biology to the interaction of biological surfaces with aqueous solutions. His 1990 book, Muscles and Molecules: Uncovering the Principles of Biological Motion, won an "Excellence Award" from the Society for Technical Communication; his more recent book, Cells, Gels and the Engines of Life, won that Society's "Distinguished Award." Pollack received an honorary doctorate in 2002 from Ural State University in Ekaterinburg, Russia, and was more recently named an Honorary Professor of the Russian Academy of Sciences. He received the Biomedical Engineering Society's Distinguished Lecturer Award in 2002. In 2008, he was the faculty member selected by the University of Washington faculty to receive their highest annual distinction: the Faculty Lecturer Award. Pollack is a Founding Fellow of the American Institute of Medical and Biological Engineering and a Fellow of both the American Heart Association and the Biomedical Engineering Society. He is also Founding Editor-in-Chief of the journal, WATER, and has recently received an NIH Transformative R01 Award. He was the 2012 recipient of the Prigogine Medal and in 2013 published his new book: The Fourth Phase of Water: Beyond Solid, Liquid, and Vapor.
Colbert Sesanker's insight:

Tensegrity, cytosociology and the gel like phase of water all contribute to the long held hypothesis that the cell is a solid state structure (see Tensegrity below).

 

 

Frohlich proposed the 'microtrabecular lattice' as the substrate of energy transfer for coherent excitations in the early 80s. The microtrabecular lattice idea WAS an artifact of early HVEM during this time. Interestingly, a solid-state cell would support coherent excitations of polar molecules (cell is saturated with these) and membranes. In this sense, it is an updated version of the microtrabecular concept. 

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Pollack Laboratory - Water and Cell Biology

Pollack Laboratory - Water and Cell Biology | Life Science Paradigms and Foundations | Scoop.it
The book, Cells, Gels and the Engines of Life builds on the central role of water for biology. It provides evidence that much of the water in the cell is very near to one or another hydrophilic surface and therefore ordered, and that cell behavior can be properly understood only if this feature is properly taken into account. It goes on to show that seemingly complex behaviors of the cell can be understood in simple terms once a proper understanding of water and surfaces is achieved.

While the book is an award-winning best seller, it has aroused controversy because it questions some long-held basic features of cell function such as membrane channels and pumps. This steps on many scientific toes. Many others have praised the insights obtained from building on a foundation of first principles (see book website above). One prominent reviewer from Harvard University opines that the book is “a 305 page preface to the future of cell biology.”
Colbert Sesanker's insight:

Reads like a children's book in a good way (super clear and non-technical). Describes a novel 4th phase of water and biological implications.

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Ingber Lab (Harvard)

Ingber Lab (Harvard) | Life Science Paradigms and Foundations | Scoop.it
Cellular Tensegrity Theory. Cells and tissues are organized as discrete network structures, and they use tensegrity architecture to mechanically stabilize themselves. In the cellular tensegrity theory, complex mechanical behaviors in cells and tissues emerge through establishment of a mechanical force balance between different molecular elements in the cytoskeleton and ECM that maintains the cell in a state of isometric tension.

Solid-State Biochemistry. Many of the biochemical events that mediate cell metabolism and signal transduction proceed using solid-state biochemistry. The enzymes and substrates that mediate these biochemical reactions are physically immobilized on insoluble molecular scaffolds within the cytoskeleton, nucleus and ECM.

Integrins as Mechanotransducers. Mechanical forces impact cellular signal transduction and influence cell decision making based on their transmission across cell surface adhesion receptors, such as integrins, that mechanically couple extracellular molecular scaffolds to the internal cytoskeleton. Mechanical forces are converted into chemical and electrical signals through stress-dependent distortion of molecules that associate with load-bearing elements of the cytoskeleton.
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Journal of Theoretical Biology (1981): On the "cytosociology" of enzyme action in vivo: a novel thermodynamic correlate of biological evolution

J Theor Biol. 1981 Dec 21;93(4):701-35.
Colbert Sesanker's insight:

The idea here is that an enzyme stripped of its 'cytosociology' is merely a shadow of its identity. It's naturally highly diverse environment is the key to its functioning. 

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Whatever happened to the ‘microtrabecular concept’? - Heuser - 2012 - Biology of the Cell - Wiley Online Library

Keith Porter culminated his stellar career as the founding father of biological electron microscopy by acquiring, in the late 1970s, a high-voltage electron microscope (HVEM). With this magnificent instrument he examined whole-mounts of cultured cells, and perceived within them a structured cytoplasmic matrix he named the “microtrabecular lattice”. Over the next decade Porter published a series of studies, together with a team of outstanding young colleagues, which elaborated his broader “microtrabecular concept.” This concept posited that microtrabeculae were real physical entities that represented the fundamental organization the cytoplasm, and that they were the physical basis of cytoplasmic motility and of cell-shape determination. The present review presents Porter's original images of microtrabeculae, after conversion to a more interpretable “digital-anaglyph” form, and discusses the rise and fall of the microtrabecular concept. Further, it explains how the HVEM images of microtrabeculae finally came to be considered as an artifact of the preparative methods Porter used to prepare whole cells for HVEM. Still, Keith's “microtrabecular concept” foretold of our current appreciation of the complexity and pervasiveness of the cytoskeleton, which has now been found by more modern methods of EM to actually be the fundamental organizing principle of the cytoplasmic matrix. During the impending eclipse of Porter's microtrabecular concept in the late 1980s, many of Keith's colleagues fondly described the cell as being filled, not with protoplasm, but with “Porterplasm.” Despite the fact that Keith's view was clouded by the methods of his time, it would be fitting and apt to retain this name, still today, for the ordered matrix of cytoskeletal macromolecules that exists in the living cell. In the end, the story of what happened to Porter's microtrabecular concept should be an object lesson in scientific hubris that should humble and inform all of us in cell biology, even today — particularly when we begin to think that our most recent methods and observations are achieving “the last word”.
Colbert Sesanker's insight:

frohlich proposed this lattice as the substrate of energy transfer for coherent excitations in the early 80s. Interestingly, the microtrabecular lattice idea WAS an artifact of early HVEM during this time. Originally proposed for the medium of excitations before the solid state conception solidified. 

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Life as Evolving Software, Greg Chaitin at PPGC UFRGS

"Few people remember Turing's work on pattern formation in biology (morphogenesis), but Turing's famous 1936 paper On Computable Numbers exerted an immense influence on the birth of molecular biology indirectly, through the work of John von Neumann on self-reproducing automata, which influenced Sydney Brenner who in turn influenced Francis Crick, the Crick of Watson and Crick, the discoverers of the molecular structure of DNA. Furthermore, von Neumann's application of Turing's ideas to biology is beautifully supported by recent work on evo-devo (evolutionary developmental biology). The crucial idea: DNA is multi-billion year old software, but we could not recognize it as such before Turing's 1936 paper, which according to von Neumann creates the idea of computer hardware and software."

 

Colbert Sesanker's insight:

The most interesting part is the relationship between innovation and incompleteness. There is too much deification of the static DNA sequence. Regardless, the only thing this abstraction requires is that the heritable information can be represented as a binary sequence.

 

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IEEE Spectrum: Machine-Learning Maestro Michael Jordan (Berkley) on the Delusions of Big Data and Other Huge Engineering Efforts

IEEE Spectrum: Machine-Learning Maestro Michael Jordan (Berkley) on the Delusions of Big Data and Other Huge Engineering Efforts | Life Science Paradigms and Foundations | Scoop.it

"Why We Should Stop Using Brain Metaphors When We Talk About Computing.

 

Spectrum: It’s always been my impression that when people in computer science describe how the brain works, they are making horribly reductionist statements that you would never hear from neuroscientists. You called these “cartoon models” of the brain.

 

Michael Jordan: I wouldn't want to put labels on people and say that all computer scientists work one way, or all neuroscientists work another way. But it’s true that with neuroscience, it’s going to require decades or even hundreds of years to understand the deep principles. There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems."

Colbert Sesanker's insight:

finally someone calls out the lost that think the brain is one giant deep neural network or (gasp) a hierarchical hidden markov model (kurzweil in 'How to Build a Mind'). 'What is thought', by Eric Baum, is another excellent book summarizing materialistic (computational and evolutionary) views of intelligence (though I think it's optimism on the feasibility of strong AI is in error).

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