Jump to content

Techne

Members
  • Content Count

    15
  • Joined

  • Last visited

Community Reputation

10 Neutral

About Techne

  • Rank
    Quark
  1. Yeah Nick Bostrom is an interesting person. The Simulation Argument FAQ
  2. pioneer, Thank you for the article. Here is post discussing that possibility. Quantum Computing in DNA Also thought this was interesting: Biological (Qu)bits: Theory and Experiment
  3. Hi swansont, I hope to get a discussion going on how one can go about testing it. Perhaps someone here has knowledge about molecular simulations and the possible effect quantum decoherence has on the structure of tubulin complexes and how these effects might alter cell signaling. Theoretically it should be possible to test it, the software, math and physics might not be up to scratch yet. However, if you, or anyone else feel it should be moved to the P&S section, I have no objections.
  4. lethierbelight, did you take part in this study? Look Who's Irrational Now
  5. Memetic Algorithms Memetic Algorithms (MAs) (good paper) are search techniques used to solve problems by mimicking molecular processes of evolution including selection, recombination, mutation and inheritance. A few important aspects of MAs (Figure 1): The fitness landscape needs to be finite. The search space of the MA is limited to the fitness landscape. There is at least one solution in the fitness landscape (Figure 2). A fitness function determines the relationship between the fitness of the genotype (or phenotype) and the fitness landscape. Selection is based on fitness. Figure 1: Basic lay out of memetic algorithms. A population of individuals is randomly seeded with regard to fitness (initialized). The individuals are randomly mutated and their fitness is measured. Individuals with optimal fitness are further mutated until convergence of a local optima is reached. The process is carried out for the entire initialized population. The global optima is selected from the various local optima. Figure 2: Fitness landscape with local optima (A, B and D) and a global optima ©. In a memetic algorithm, the initial population of individual are randomly seeded and can be viewed as any of the arrows indicated in the figure. Various molecular docking programs employ genetic algorithms in order to try and predict the orientation of a ligand within a protein receptor. Autodock employs a MA for this purpose. A good docking program is one that can reproduce an existing crystallographic pose with reasonable success. The Root Means Squared Deviation (RMSD) of a docked ligand compared the to the crystallographic pose is generally used as a good indicator. A RMSD value less than 2 is considered a success. In the case of the Autodock software, the global optima is supposed to correlate with the crystallographic pose (RMSD <2) As an example to illustrate, Colchicine binds to tubulin and interferes with tubulin dynamics by inhibiting tubulin polymerization. Colchicine binds at a position between the alpha and beta tubulin dimer (Figures 3 and 4). Figure 3: Colchicine binding site. Figure 4: Colchicine binding cavity. A docking run with Autodock can be characterized by the following: Finite fitness landscape: The physical properties of the protein receptor (E.g. electrostatic properties, Van der Waals interactions and desolvation energies). Pre-existing fitness landscape. Search space: Confined to the protein receptor. At least one solution: Crystallographic pose. Fitness function: Estimated Free Energy of Binding pose. This is determined through a combination of various interactions including Van der Waals-, electrostatic-, desolvation-, hydrogen bond- and torsional free energy. Selection (guiding function): Selection is based on fitness. Using Autodock, Colchicine was "docked" 4 times into the tubulin receptor. Each time the ligand is docked, 30 populations with 250 individuals (ligands) are randomly placed within the receptor. The local optima of each population is determined (blue bar graph). The results revealed the following (Figures 5a-d). Figure 5a: Run 1 Figure 5b: Run 2 Figure 5c: Run 3 Figure 5d: Run 4 All four runs converged on a the same global optima which also corresponded reasonably well to the crystallographic pose (RMSD<1.8). Various local optima were also repeatedly reached. Can this process of evolution be viewed to be analogous to the evolution of life? A few observations: A) The Memetic Algorithms of life. a) A genetic code that is optimized for random searches. b) Quality control systems (DNA repair, protein quality, programmed cell death, cell cycle control). c) Variation inducers (Cytosine deaminases, Low vs High fidelity polymerase induction, gene conversion, homologous recombination). B) Examples of convergence in the evolution of life. Running MAs in pre-existing fitness landscapes result in the convergence of various local optima, with the global optima being the best of the local optima. Evolutionary history is filled with examples of convergence (local optima). These include the following: a) The spectacular convergence of abiogenesis into a universal optimized genetic code and life's memetic algorithms. b) Structural convergence: Nice article showing a sundry of examples of convergent evolution. c) Molecular convergence Carbonic anhydrases Prestin More examples C) Pre-existing fitness landscapes and the evolution of life. The fitness of the docking pose of the ligand in the above example is dependent on the pre-existing properties of the receptor protein. These properties include: Van der Waals energy Electrostatic energy Desolvation energy Hydrogen bond energy Torsional free energy These are all combined to determine the fitness (binding energy) of the ligand (Figure 6). Figure 6: Convergence of local optima of Colchicine in the pre-existing fitness landscape of the tubulin protein receptor Fitness (binding energy) is measured by Van der Waals-, Electrostatic-, Desolvation-, Hydrogen bond - and Torsional free energy. Replaying the docking run yields similar results every time. Standard evolutionary theory describes fitness as the capability of an individual of a certain genotype to reproduce. What are the properties of the pre-existing fitness landscape of life that determines the fitness of life forms? Should these properties include the following? Self-replication Intelligence (Ability to process information - genetics, proteomics, metabolomics) Agency (Ability to manipulate information) Complexity (Emergence of complexity seems to be the first rule of evolution) What are these properties composed of? Perhaps elemental proto-experiences (PEs) as phenomenal aspects that are properties of elementary particle (superimposed) described in this paper? Can it connect quantum physics, consciousness (article) and evolution? A "docking" (replaying the tape of life) run with such a simulation can be characterized by the following : Finite fitness landscape: The physical properties of the universe (Mass, spin, charge and proto-experiences superimposed as elementary particles. The pre-existing fitness landscape. Search space: Confined to the universe. At least one solution: Self-replication. Fitness function: Reproduction success. This is determined through a combination of various interactions including self-replication, intelligence, agency and emergence of complexity. Selection (guiding function): Selection is based on fitness. What would a "docking" run of life look like if we run it over and over with a pre-existing fitness landscape and universal memetic genetic algorithms (Figure 7)? Figure 7: Convergence of local optima in a fitness landscape whereby fitness is measured by reproduction, intelligence, agency and complexity. If life's memetic algorithms are comparable to a "docking" run, it should yield similar local optima in pre-existing fitness landscapes every time the simulation is run. Any thoughts?
  6. Techne

    Preadaptations

    Hi pioneer, Perhaps these "illusions" of foresight and preadaptations are due to our biomolecular machines ? More preadaptations: Sponges (wiki): Nice site about sponges, Evolutionary history of sponges (Sponges = light blue, Divergence time = yellow) Choanoflagellates had a lot of the toolkits necessary to develop a nervous system as well as multi-cellularity, even though they are simple uni-cellular organisms that do not form colonial assemblages. Now the Origin of Nerves are Traced to Sponges Awesome . Free, online peer-reviewed article: A Post-Synaptic Scaffold at the Origin of the Animal Kingdom There are even more fascinating findings from the genome of the sponge. Article with the details: Article abstract: Sponge Genes Provide New Insight into the Evolutionary Origin of the Neurogenic Circuit Whole parts of the nervous system were present in animals that do not have a nervous system, yet these parts are interchangeable and function just like they should in animals that do have a nervous system.
  7. Welcome to the Molecular Machines A thread to lump together all the interesting discoveries regarding the intracellular biomolecular machinery that are crucial for life to exist. Please post interesting discoveries and perhaps describe the functionality of these intracellular biomolecular machines. Intracellular biomolecular machinery include the following: 1) DNA replication and repair machinery (replisome) 2) DNA transcription machinery and RNA processing and translation machinery (Spliceosomes and ribosomes) 3) Cell cycle signaling network (pRB, e2F, CDKs) 4) Programmed cell death machinery (Apoptosis, autophagy, mitotic catastrophe) 5) Protein processing machinery (Chaperones, ubiquitin-proteasome system) 6) Intracellular signaling networks (protein kinases and phosphatases) 7) Mechanical machines for intracellular shuttling of biomolecules and cellular movement (Microtubule network, kinesin, dynein) 8) Energy production machines (Electron transport chain, F0F1 ATP synthase) The Replisome: Sliding clamps, clamp-loaders and helicases. Sliding clamps are ring-shaped proteins that some refer to as the “guardians” of the genome or others name them as the “ringmasters” of the genome. Interestingly these clamps are structurally and functionally conserved in all branches of life and crystallographic studies have shown that they have almost superimposable three-dimensional structures, yet these components have very little sequence similarity (Figure 1) [1]. Figure 1: Sliding clamps from the various domains of life. What do they do? The picture below is taken from the Molecular Biology Visualization of DNA video (2:14) from the freesciencelectures.com site. Great video! Figure 2: Replication machinery. The following components can be seen. Sliding clamps (PCNA in eukaryotes): Green circular shaped Clamp loader (RFC in eukaryotes): Blue-white component in the middle (Figure 3: Structures of PCNA connected to RFC (front)) (Figure 4: Structures of PCNA connected to RFC (side)) (Figure 5: Structures of PCNA connected to RFC (back)) Helicase: Blue (Figure 6: Helicase (front)) DNA polymerase: Dark-blue components attached to the sliding clamps Primase: Green component attached to helicase Leading strand: Spinning off to the right Lagging strand: Spinning off to the top They are not ringmasters for nothing. Sliding clamps participate and control events that orchestrate DNA replication events in the following ways: Enhancement of DNA polymerase activity. Coordinate Okazaki fragment processing. Prevention of rereplication Translesion synthesis Prevents sister-chromatid recombination and also coordinates sister-chromatid cohesion Crucial role in mismatch repair, base excision repair, nucleotide excision repair Participates in chromatin assembly Other functions include: Epigenetic inheritance Chromatin remodeling Controls cell cycle and cell death signaling The true ringmasters. Clamp loaders are another group of interesting proteins (see video and figures 3-5 above). Interestingly again, their functional and structural architecture are conserved across the three domains of life with low-level sequence similarity [2]. At the replication fork during replication, they load the sliding clamps many times onto the lagging strand (after DNA priming) and only once onto the leading strand. They also act as a bridge to connect the leading and lagging strand polymerases and the helicase. Which brings us to another interesting group of proteins; the helicases. Helicases are also known to be ring-shaped motor proteins, typically hexamers (see figure 6) and separate double-stranded DNA into single-stranded templates for the replication machinery. Replication occurs at about 1000 base pairs per second due to the highly efficient combination of sliding clamps and the polymerases. Thus, helicases need to unwind DNA at at least that speed. Unwinding DNA too slowly and the replication machinery might break down . Unwind the DNA too fast or untimely and harmful mutations might occur as single-stranded DNA is prone to degradation and cytosine deamination. The speed at which helicase unwinds DNA is no accident though, as it is intrinsically controlled. As helicase is bound to the lagging strand, it unwinds the leading strand in a separate direction. Applying a pulling force on the leading strand leads to a 7-fold increase in the speed of DNA unwinding by helicase [3, 4]. The highly efficient DNA polymerase/sliding clamp combination provides this controlling force on the leading strand. This forms a robust unwinding/polymerization interaction whereby polymerization controls and prevents unwanted DNA unwinding. Altogether, the replisome machinery provide a robust way for DNA replication to prevent unnecessary DNA damage and mutation. References 1. Vivona JB, Kelman Z. The diverse spectrum of sliding clamp interacting proteins. FEBS Lett. 2003 Jul 10;546(2-3):167-72. 2. Jeruzalmi D, O'Donnell M, Kuriyan J. Clamp loaders and sliding clamps. Curr Opin Struct Biol. 2002 Apr;12(2):217-24. 3. Ha T. Need for speed: mechanical regulation of a replicative helicase. Cell. 2007 Jun 29;129(7):1249-50. 4. Johnson DS, Bai L, Smith BY, Patel SS, Wang MD. Single-molecule studies reveal dynamics of DNA unwinding by the ring-shaped T7 helicase. Cell. 2007 Jun 29;129(7):1299-309.
  8. Quantum physics and Consciousness. Are they connected? The microtubule connection. Research into the brain-body-mind problem is ongoing and one way of attempting to understand it is to try and describe consciousness in terms of material particles and fields interacting between inputs, internal states, and outputs without any intrinsic meaning. Terms such as “feeling”, “intention”, “knowing” and “choice” are thus not viewed as primary causal factors of consciousness, but a byproduct of these blind interactions. Quantum physics has not been at the forefront to attempt to describe consciousness as the neurocomputational model of laterally connected input layers of the brain’s neurocomputational architecture is viewed as the most credible explanation for consciousness. One problem that quantum mechanics face is the effect of quantum decoherence (The Role of Decoherence in Quantum Mechanics) and failures to measure it. Essentially, quantum states are believed to be too sensitive and fragile to disruption by thermal energy to affect the macroscopic nature of proteins and other macromolecular structures. The Penrose-Hameroff orchestrated objective reduction (orch. OR) model provides a basis to connect consciousness with quantum mechanics. Microtubules are integral in this theory. Connecting quantum mechanics, aromatic ring pi-bonds, protein formation, microtubules and consciousness. The “quantum physics” and “aromatic ring pi bond” connection. An aromatic (aromaticity) compound is composed of a conjugated planar ring system with delocalized pi electron clouds. Benzene is an example of an aromatic compound (Figure 1). In benzene (and other aromatic compounds) the double bonds are shorter than the single bonds, causing the carbon atoms to be pulled and pushed between two states and thus vibrate between two states (Figure 2). The pi electrons are also delocalized above and below the carbon ring (Figure 1). Aromatic compounds are thus described to be resonating and are best described quantum mechanically. The “aromatic ring pi bond” and “protein formation” connection. 4 amino acids contain aromatic rings: tyrosine, phenylalanine, tryptophan and histidine (Figure 3). Histidine, however has 6 delocalized electrons but not a benzene ring and is hydrophilic (more polar). When peptide chains fold to form proteins, the structure is stabilized and dynamically regulated in the intracellular aqueous phase. Polar side groups face outwardly and react with the polar aqueous milieu, while non-polar regions face inwardly (Protein folding). Aromatic amino acids are more non-polar and thus coalesce more readily in the centre of a protein. When aromatic amino acids coalesce it allows London force van der Waals interactions between the non-polar electron clouds of the aromatic rings, causing quantum resonation of the coalesced non-polar aromatic rings (Figure 4). The “protein formation” and “microtubule” connection. Microtubules are long, hollow, cylindrical, filamentous, tube-shaped protein polymers consisting of alpha and beta tubulin dimers and form part of the cytoskeleton (Figure 5, Figure 6, Figure 7). Microtubules play important roles in cell signaling, cell division and mitosis, vesicle and mitochondrial transport and play crucial roles in the development and maintenance of cells and cell shape. Microtubules are highly dynamic cytoskeletal fibres and are capable of two types of dynamics: 1) Treadmilling and 2) Dynamic instability Microtubules polymerize (rescue/elongate) at the positive (+) end and depolymerize (catastrophe/shorten) at the negative (-) end. During treadmilling, polymerization and depolymerization occur at equal rates and thus the microtubules do not change in length but changes position 4-dimensionally. During dynamic instability, either the (+) end polymerizes quicker than the (-) end can depolymerize resulting in total elongation of the microtubule, or the (-) end depolmerizes quicker than the (+) end can polymerize resulting in total shortening of the microtubule. In the Inner Life of the Cell video this behavior can be witnessed at time approx 1.07-1.11min (rescue) and 1.11-1-15 (catastrophe). Figure 8 shows the structure of the alpha- and beta-tubulin dimers and the prevalence of aromatic amino acids (1sa0.pdb). At a higher resolution (Figure 9) it is clear that the aromatic amino acids are close enough to each other (< 2nM) to allow for London van der Waals (Figure 10) interactions. When tubulins polymerize during dynamic instability (rescue) they form tube-like structures (Figure 7). Quantum level resonance as a result of quantum level dipole oscillations (London van der Waals forces) within hydrophobic pockets result in functional protein vibrations which depend on quantum effects (Figure 11). The quantum effect on a single tubulin protein conformation is superposed and exists in both states simultaneously and acts as a qubit (as in quantum computer). Thus, the elegant formation of microtubules (Figure 7 and Figure 12) can in theory constitute a quantum computer (more detail). The “microtubule” and “consciousness” connection. Microtubules extend throughout dendrites and axons (neural cells) and play crucial roles in controlling synaptic strengths responsible for learning and cognitive functions through mechanical signaling, communication as well as cytoskeletal scaffolding (cell movement). In a nutshell, the Penrose-Hameroff orch OR model proposes that quantum effects are relayed through pi-bonds in hydrophobic pockets within microtubules to the macroscopic structure of the brain, resulting in consciousness. Microtubules are thus viewed as protein quantum computers relaying the information locked in Planck scale. Fascinating! Off course the detail of this model is much more in depth and the following documents and web pages illustrates it beautifully. Enjoy!!! 1) Quantum consciousness 2) The Brain Is Both Neurocomputer and Quantum Computer 3) That's life! The geometry of pi electron resonance clouds. 4) Quantum computation in brain microtubules? The Penrose-Hameroff "Orch OR" model of consciousness 5) Microtubules - Nature's Quantum Computers?
  9. Techne

    Preadaptations

    Preadaptations (aka exaptations) are features that perform a function but was not produced by natural selection for its current use. The word "preadaptation" was co-opted () into "exaptation", however Daniel Dennett denies exaptation differs from preadaptation. A simple example of a preadaptation is a feather that evolved (through natural selection) for warmth and was coopted into a new function, flight. The genomes of various ancient organisms have been sequenced and it is interesting to view the presence of several preadaptations in the genomes of these creatures. The purpose of this thread is to highlight several of these interesting findings. If anyone come across any interesting findings, post it here . Various trees of life exist. For example: 1 2 3 4 5 6 7 For the purpose of this thread, tree #2 (Dhushara, trevol.jpg) will be used as it is a nice representation of the evolution of animals (especially vertebrates). Horizontal gene transfer and endosymbiotic events are however not clear and tree #7 (Doolittle) is probably a better way of looking at evolution. Therefore keep #2 and #7 in mind and try and piece them together. Preadaptations in the genome of the choanoflagellate, Monosiga brevicollis: Choanoflagellates (link) are single-celled organisms thought to be most closely related to animals. The divergence time of this organism was about >600 million years ago (Link) (Blue circle in image). Tyrosine Kinases are crucial for multicellular life to exist and play pivotal roles in diverse cellular activities including growth, differentiation, metabolism, adhesion, motility, death (link). More than 90 Protein Tyrosine Kinases (PTKs) have been found in the human genome. Interestingly Monosiga brevicollis has a tyrosine kinase signaling network more elaborate and diverse than found in any known metazoan. Adherens junctions are also crucial components of multicellular life and function to communicate and adhere together in tissues. Even though Monosiga brevicollis are single-celled and do not form colonial assemblages, it is interesting to know they posses about 23 cadherins genes (Cadherins) usually associated with multicellular organisms. Calcium signaling toolkits also play a crucial role in multicellular signaling. Calcium signaling plays a crucial part in contraction, metabolism, secretion, neuronal excitability, cell death, differentiation and proliferation. Thus, it is also interesting to note that Monosiga brevicollis has an extensive calcium signaling toolkit and emerged before the evolution of multicellular animals. Tyrosine kinases, calcium signaling, and adherens junctions all play a part in neural signaling and other multecellular systems. Monosiga brevicollis does not have a nervous system. Thus it is also interesting to find the presence of the hedgehog gene in the genome of Monosiga brevicollis. Signaling by Sonic hedgehog (Shh) controls important developmental processes, including neural stem cell proliferation. (Link). Nice article: Multigene Phylogeny of Choanozoa and the Origin of Animals Compare the hedgehog gene of Monosiga brevicollis to that of humans. Another interesting fact about the genome of the Monosiga brevicollis is noted in this article. Fascinating multicellular preadaptations very early on in the evolution of single-celled organisms. Next a look at sponges.
  10. Techne

    Junk DNA

    I thought these findings were interesting: Deletion of Ultraconserved Elements Yields Viable Mice The four sequences that were knocked out in this study had no visible immediate effect on fitness in the mice. Interestingly, one of the sequences (uc467) is found in the reptile, Carolina anole. Use this site to blast the uc467 sequence in eukatyotes. It would be interesting to see what the function of this sequence is in the Carolina anole genome and whether deletion of the sequence will have any effect on fitness. Any thoughts why these sequences where ultraconserved and ultraselected without having any effect on fitness? Redundant copying error propagated through millions of years?
  11. Found this to be interesting. The Simulation Argument (SA). Paper by Nick Bostrom: ARE YOU LIVING IN A COMPUTER SIMULATION? Abstract The conclusion sounds fun. Conclusion: Emphasis mine. Also, a nice blog entry about the SA. The dark side of the Simulation Argument And something for laughs. The old brain-in-a-vat argument, rehashed. Damned if it is true, damned if it is not. Have fun.
  12. Techne

    Ageing

    Ageing (from wiki): Why do organisms age (wiki again): Recent scientific discoveries are however challenging this understanding of ageing. Prevailing Theory Of Aging Challenged: Genetic Instructions Found To Drive Aging In Worms If ageing is pre-programmed, it should theoretically be possible to reprogram genetic software to make you live longer. All the software is there to make you live for longer, the trick is now to discover which signaling pathways need to be tweaked and how to manipulate these pathways.
  13. Hi, New user here. Nice science forum. Thought I'd drop by and try and post interesting things related to biochemistry, molecular biology and evolution. Regards.
×
×
  • Create New...

Important Information

We have placed cookies on your device to help make this website better. You can adjust your cookie settings, otherwise we'll assume you're okay to continue.