Mystery at the heart of life

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December 21, 2014

Cell biology, Intelligent Design, News

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Cell biology
Intelligent Design
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By Biologic Institute’s Ann Gauger, at Christianity Today’s Behemoth, the secret life of cells:

Our bodies are made up of some 100 trillion cells. We tend to think of cells as static, because that’s how they were presented to us in textbooks. In fact, the cell is like the most antic, madcap, crowded (yet fantastically efficient) city you can picture. And at its heart lies a mystery—or I should say, several mysteries—involving three special kinds of molecules: DNA, RNA, and proteins.

These molecules are assembled into long chains called polymers, and are uniquely suited for the roles they play. More importantly, life absolutely depends upon them. We have to have DNA, RNA, and protein all present and active at the same time for a living organism to live.

How they work together so optimally and efficiently is not merely amazing, but also a great enigma, a mystery that lies at the heart of life itself. More. Paywall soon after. May be worth it.

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Proposed “top down” frameworks for understanding neural computation include entropy maximization, efficient encoding, faithful approximation of Bayesian inference, minimization of prediction error, attractor dynamics, modularity, the ability to subserve symbolic operations, and many others [...]
Toward an Integration of Deep Learning and Neuroscience Adam H. Marblestone, Greg Wayne and Konrad P. Kording Front Comput Neurosci. 10: 94. doi: 10.3389/fncom.2016.00094

Did somebody say "top-down"? :) Glad to see they could eventually realize that biological systems were "top-down" designed. Complex complexity.Dionisio_{January 29, 2017
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Due to the complexity and variability of the brain, pure “bottom up” analysis of neural data faces potential challenges of interpretation (Robinson, 1992; Jonas and Kording, 2016).
Toward an Integration of Deep Learning and Neuroscience Adam H. Marblestone, Greg Wayne and Konrad P. Kording Front Comput Neurosci. 10: 94. doi: 10.3389/fncom.2016.00094

We've been saying for quite a while that it's not very efficient to do bottom-up reverse engineering of biological systems that were designed top-down. Complex complexity.Dionisio_{January 29, 2017
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When did the division between cost functions and optimization algorithms occur? How is this separation implemented? How did innovations in cost functions and optimization algorithms evolve? And how do our own cost functions and learning algorithms differ from those of other animals?
Toward an Integration of Deep Learning and Neuroscience Adam H. Marblestone, Greg Wayne and Konrad P. Kording Front Comput Neurosci. 10: 94. doi: 10.3389/fncom.2016.00094

At least some references to pseudoscientific nonsense is included in many otherwise-excellent papers in order to get the seal of approval of the establishment. That's pathetically bizarre, but it's the reality we see. Complex complexity.Dionisio_{January 29, 2017
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Machine learning and neuroscience speak different languages today. Brain science has discovered a dazzling array of brain areas (Solari and Stoner, 2011), cell types, molecules, cellular states, and mechanisms for computation and information storage. Machine learning, in contrast, has largely focused on instantiations of a single principle: function optimization. We will argue here, however, that neuroscience and machine learning are again ripe for convergence.
Toward an Integration of Deep Learning and Neuroscience Adam H. Marblestone, Greg Wayne and Konrad P. Kording Front Comput Neurosci. 10: 94. doi: 10.3389/fncom.2016.00094

Complex complexity.Dionisio_{January 29, 2017
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Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior.
Toward an Integration of Deep Learning and Neuroscience Adam H. Marblestone, Greg Wayne and Konrad P. Kording Front Comput Neurosci. 10: 94. doi: 10.3389/fncom.2016.00094

Complex complexity.Dionisio_{January 29, 2017
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[...] the functional and theoretical benefits of networks of neurons with active dendrites as compared to a multi-layer network of neurons without active dendrites are unclear [...] [...] we need an understanding of how biological neurons integrate input from thousands of synapses and whether active dendrites play an essential role. [...] we also need a complementary theory of how networks of neurons, each with active dendrites, work together toward a common purpose.
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Hawkins J, Ahmad S Front Neural Circuits. 10:23. doi: 10.3389/fncir.2016.00023

Work in progress... stay tuned. Complex complexity.Dionisio_{January 29, 2017
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[...] pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Hawkins J, Ahmad S Front Neural Circuits. 10:23. doi: 10.3389/fncir.2016.00023

Complex complexity.Dionisio_{January 29, 2017
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We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations.
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Hawkins J, Ahmad S Front Neural Circuits. 10:23. doi: 10.3389/fncir.2016.00023

Complex complexity.Dionisio_{January 29, 2017
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[...] patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts.
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Hawkins J, Ahmad S Front Neural Circuits. 10:23. doi: 10.3389/fncir.2016.00023

Complex complexity.Dionisio_{January 29, 2017
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[...] a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation.
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Hawkins J, Ahmad S Front Neural Circuits. 10:23. doi: 10.3389/fncir.2016.00023

Complex complexity.Dionisio_{January 29, 2017
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Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue.
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex. Hawkins J, Ahmad S Front Neural Circuits. 10:23. doi: 10.3389/fncir.2016.00023

Did somebody say "mystery"? :) Complex complexity.Dionisio_{January 29, 2017
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The surprising power of spike timing to predict behavior might reflect synchrony between motor units in the respiratory muscles [...] [...] respiration is driven by a brainstem central pattern generator (CPG) but modified by descending inputs from the forebrain [...]. It remains unknown which of these is the source of timing precision/variability [...]
Motor control by precisely timed spike patterns Kyle H. Srivastava, Caroline M. Holmes, Michiel Vellema, Andrea R. Pack, Coen P. H. Elemans, Ilya Nemenman and Samuel J. Sober PNAS doi: 10.1073/pnas.1611734114

Complex complexity.Dionisio_{January 29, 2017
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The brain uses sequences of spikes to encode sensory and motor signals. ...] it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. [...] precise cortical spike timing contains much more information about upcoming behavior than does rate. [...] it remains unclear whether spike timing actually controls variations in behavior. [...] cortical neurons upstream of vocal and respiratory muscles also use spike timing to encode behavior [...] [...] precise spike timing predicts behavioral variations [...] [...] the small, precisely-regulated differences in motor neuron spike patterns in vivo cause muscles to produce different forces. [...] respiratory motor unit activity is controlled on millisecond timescales [...]
Motor control by precisely timed spike patterns Kyle H. Srivastava, Caroline M. Holmes, Michiel Vellema, Andrea R. Pack, Coen P. H. Elemans, Ilya Nemenman and Samuel J. Sober PNAS doi: 10.1073/pnas.1611734114

Complex complexity.Dionisio_{January 29, 2017
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A crucial problem in neuroscience is understanding how neural activity (sequences of action potentials or “spikes”) controls muscles, and hence motor behaviors. In principle, neurons can encode this information via their firing rates, the precise timing of their spikes, or both [...] [...] the precise timing of spikes, rather than just their number, plays a crucial role in predicting and causally controlling behavior. [...] basic assumptions about neural motor control require revision [...]
Motor control by precisely timed spike patterns Kyle H. Srivastava, Caroline M. Holmes, Michiel Vellema, Andrea R. Pack, Coen P. H. Elemans, Ilya Nemenman and Samuel J. Sober PNAS doi: 10.1073/pnas.1611734114

Complex complexity.Dionisio_{January 29, 2017
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The developmental systems view also informs how we think about the brain. Some researchers insist that the brain is a modular system, hardwired for specialized abilities. But recent findings have revealed tremendous plasticity, particularly early in development. At the extreme, plasticity can take hold, restoring complex cognitive functions even when infants experience substantial brain damage due to stroke or other health complications. Simply put, none of us is hardwired or preprogrammed. Each of us develops.
Introduction to the collection ‘How We Develop—Developmental Systems and the Emergence of Complex Behaviors’ Mark S. Blumberg, John P. Spencer, David Shenk DOI: 10.1002/wcs.1413 WIREs Cogn Sci, 8: n/a, e1413

Complex complexity.Dionisio_{January 28, 2017
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The new understanding starts with a new conception of the gene. Genes are not like automatons, reciting the same lines in exactly the same way regardless of changing circumstances. Instead, they are more like jazz musicians, interacting with their surroundings from moment to moment in complex and often surprising ways. Beyond this more dynamic view of gene action, the developmental systems perspective provides a broad framework for thinking about individual development at multiple levels (molecular, neural, and behavioral) and timescales.
Introduction to the collection ‘How We Develop—Developmental Systems and the Emergence of Complex Behaviors’ Mark S. Blumberg, John P. Spencer, David Shenk DOI: 10.1002/wcs.1413 WIREs Cogn Sci, 8: n/a, e1413

Complex complexity.Dionisio_{January 28, 2017
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[...] a new ‘systems’ view of research in such domains as fetal development, neuroplasticity, the functional organization of the brain, and cognition suggests that the old debates about nature and nurture should be thrown out in favor of something new—a unified ‘developmental systems’ perspective.
Introduction to the collection ‘How We Develop—Developmental Systems and the Emergence of Complex Behaviors’ Mark S. Blumberg, John P. Spencer, David Shenk DOI: 10.1002/wcs.1413 WIREs Cogn Sci, 8: n/a, e1413

Complex complexity.Dionisio_{January 28, 2017
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In his classic essay, ‘Seven Wonders,’ the physician and essayist Lewis Thomas wrote that childhood was one of life's great mysteries. Why, he pondered, did evolution not allow us to skip childhood altogether, ‘to jump catlike from our juvenile to our adult [and] productive stage of life?’ It is indeed extraordinary how long it takes for humans to develop into mature, capable adults.
Introduction to the collection ‘How We Develop—Developmental Systems and the Emergence of Complex Behaviors’ Mark S. Blumberg, John P. Spencer, David Shenk DOI: 10.1002/wcs.1413 WIREs Cogn Sci, 8: n/a, e1413

Complex complexity.Dionisio_{January 28, 2017
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A rather disquieting characteristic of EBs and specifically gastruloids is that they appear to uncouple processes that in the embryo are tightly linked, such as specification of the anterior-posterior axis and anterior neural development or endoderm specification and axial elongation. Are these processes truly independent? Are they uncoupled as early progenitors (ESCs), when freed from some of the constraints placed on them in normal development, can opt for differentiation pathways not available in vivo? Perhaps with the right combination of imaging, a new generation of reporters and an understanding of lineage with in these structures, these questions will be answered.
Properties of embryoid bodies Joshua M. Brickman, Palle Serup DOI: 10.1002/wdev.259 WIREs Dev Biol

Work in progress... stay tuned. Complex complexity.Dionisio_{January 28, 2017
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One of the most remarkable properties of embryonic stem cells (ESCs) is their capacity to organize themselves into structures that are able to mimic some of the three-dimensional (3D) qualities of embryonic development. These so-called embryoid bodies (EBs) represent an experimental model that has provided many important clues for unraveling early embryonic development.
Properties of embryoid bodies Joshua M. Brickman, Palle Serup DOI: 10.1002/wdev.259 WIREs Dev Biol

Complex complexity.Dionisio_{January 28, 2017
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[...] formation of EBs constitutes an important initial step in directed differentiation protocols aimed at generated specific cell types from undifferentiated stem cells. Recent studies that employ modern signaling reporters and tracers of lineage commitment have revealed both the strengths and the weaknesses of EBs as a model of embryonic axis formation.
Properties of embryoid bodies Joshua M. Brickman, Palle Serup DOI: 10.1002/wdev.259 WIREs Dev Biol

Complex complexity.Dionisio_{January 28, 2017
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The history of neural plasticity research is one of surprises. Decades ago, large-scale reorganization of the adult brain was considered impossible. We now know differently. Similarly, it was also long thought that no new neurons were born in an adult brain. We now know that in at least some areas of the human brain, new neurons are born. [...] developmental plasticity once thought to be confined to early development may in the future have relevance for adulthood [...]
Neural plasticity across the lifespan Jonathan D. Power, Bradley L. Schlaggar DOI: 10.1002/wdev.216 WIREs Dev Biol, 6: n/a, e216.

Work in progress... stay tuned. Complex complexity.Dionisio_{January 28, 2017
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[...] molecular gradients, spontaneous neural activity, and peripherally driven neural activity are all critical for proper development of the brain.
Neural plasticity across the lifespan Jonathan D. Power, Bradley L. Schlaggar DOI: 10.1002/wdev.216 WIREs Dev Biol, 6: n/a, e216.

Complex complexity.Dionisio_{January 28, 2017
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Physical changes at a cellular level manifest as circuit-level changes in patterns of neuronal firing, and it is these circuit-level changes that allow us to learn, to remember, and to adapt to changing conditions of the body and environment. These physical changes in neuronal structure result from a combination of the very thoughts we have (i.e., prior patterns of neural firing), as well as genetic and biochemical influences.
Neural plasticity across the lifespan Jonathan D. Power, Bradley L. Schlaggar DOI: 10.1002/wdev.216 WIREs Dev Biol, 6: n/a, e216.

Complex complexity.Dionisio_{January 28, 2017
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An essential feature of the brain is its capacity to change. Neuroscientists use the term ‘plasticity’ to describe the malleability of neuronal connectivity and circuitry. How does plasticity work?
Neural plasticity across the lifespan Jonathan D. Power, Bradley L. Schlaggar DOI: 10.1002/wdev.216 WIREs Dev Biol, 6: n/a, e216.

Complex complexity.Dionisio_{January 28, 2017
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Articles on mitochondria: http://www.reasons.org/articles/complex-protein-biogenesis-hints-at-intelligent-design http://www.reasons.org/blogs/the-cells-design/can-a-creation-model-explain-the-origin-of-mitochondriaDionisio_{January 28, 2017
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[...] only in mammals there is an additional upregulation of dorsal and anterior signaling centers (the cortical hem and the anterior forebrain, respectively) that promoted a laminar and a columnar structure into the neocortex.
Pallial patterning and the origin of the isocortex Juan F. Montiel and Francisco Aboitiz Front Neurosci. 9: 377. doi: 10.3389/fnins.2015.00377

Complex complexity.Dionisio_{January 27, 2017
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Together with a complex variety of behavioral, physiological, morphological, and neurobiological innovations, mammals are characterized by the development of an extensive isocortex (also called neocortex) that is both laminated and radially organized, as opposed to the brain of birds and reptiles.
Pallial patterning and the origin of the isocortex Juan F. Montiel and Francisco Aboitiz Front Neurosci. 9: 377. doi: 10.3389/fnins.2015.00377

Complex complexity.Dionisio_{January 27, 2017
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Rnd proteins are atypical members of the Rho GTPase family that induce actin cytoskeletal reorganization and cell rounding. [...] plexin-B2 is a downstream target for Rnd3, which contributes to its cellular function.
Rnd3-induced cell rounding requires interaction with Plexin-B2 Brad McColl,*¶ Ritu Garg,¶ Philippe Riou,‡ Kirsi Riento,§ and Anne J. Ridley J Cell Sci. 129(21): 4046–4056. doi: 10.1242/jcs.192211

Complex complexity.Dionisio_{January 27, 2017
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It remains to be determined if disruptions to Bacurd levels alter the binding of Rnd2 and Rnd3 to their other partners in neurons (such as p190RhoGAP and Plexin B2), or influence RhoA signalling, or both.
Bacurd1/Kctd13 and Bacurd2/Tnfaip1 are interacting partners to Rnd proteins which influence the long-term positioning and dendritic maturation of cerebral cortical neurons Ivan Gladwyn-Ng, Lieven Huang, Linh Ngo, Shan Shan Li, Zhengdong Qu, Hannah Kate Vanyai, Hayley Daniella Cullen, John Michael Davis, and Julian Ik-Tsen Heng Neural Dev. 11: 7. doi: 10.1186/s13064-016-0062-1

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