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تسجيل مستخدم جديدVibrations, Quanta and Biology
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Quantum biology is an emerging field of research that concerns itself with the experimental and theoretical exploration of non-trivial quantum phenomena in biological systems. In this tutorial overview we aim to bring out fundamental assumptions and questions in the field, identify basic design principles and develop a key underlying theme -- the dynamics of quantum dynamical networks in the presence of an environment and the fruitful interplay that the two may enter. At the hand of three biological phenomena whose understanding is held to require quantum mechanical processes, namely excitation and charge transfer in photosynthetic complexes, magneto-reception in birds and the olfactory sense, we demonstrate that this underlying theme encompasses them all, thus suggesting its wider relevance as an archetypical framework for quantum biology.
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Is there a functional role for quantum mechanics or coherent quantum effects in biological processes? While this question is as old as quantum theory, only recently have measurements on biological systems on ultra-fast time-scales shed light on a pos
sible answer. In this review we give an overview of the two main candidates for biological systems which may harness such functional quantum effects: photosynthesis and magnetoreception. We discuss some of the latest evidence both for and against room temperature quantum coherence, and consider whether there is truly a functional role for coherence in these biological mechanisms. Finally, we give a brief overview of some more speculative examples of functional quantum biology including the sense of smell, long-range quantum tunneling in proteins, biological photoreceptors, and the flow of ions across a cell membrane.
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n exhibit phenomena unknown from those traditionally studied in physics. Spectacular advances in experimentation during the last decade or two, for example, in microscopy, single cell dynamics, in the reconstruction of sub- and multicellular systems outside of living organisms, or in high throughput data acquisition have yielded an unprecedented wealth of data about cell dynamics, genetic regulation, and organismal development. These data have motivated the development and refinement of concepts and tools to dissect the physical mechanisms underlying biological processes. Notably, the landscape and flux theory as well as active hydrodynamic gel theory have proven very useful in this endeavour. Together with concepts and tools developed in other areas of nonequilibrium physics, significant progresses have been made in unraveling the principles underlying efficient energy transport in photosynthesis, cellular regulatory networks, cellular movements and organization, embryonic development and cancer, neural network dynamics, population dynamics and ecology, as well as ageing, immune responses and evolution. Here, we review recent advances in nonequilibrium physics and survey their application to biological systems. We expect many of these results to be important cornerstones as the field continues to build our understanding of life.
In recent work we reported the vibrational spectrum of more than 100,000 known protein structures, and a self-consistent sonification method to render the spectrum in the audible range of frequencies (Extreme Mechanics Letters, 2019). Here we present
a method to transform these molecular vibrations into materialized vibrations of thin water films using acoustic actuators, leading to complex patterns of surface waves, and using the resulting macroscopic images in further processing using deep convolutional neural networks. Specifically, the patterns of water surface waves for each protein structure is used to build training sets for neural networks, aimed to classify and further process the patterns. Once trained, the neural network model is capable of discerning different proteins solely by analyzing the macroscopic surface wave patterns in the water film. Not only can the method distinguish different types of proteins (e.g. alpha-helix vs hybrids of alpha-helices and beta-sheets), but it is also capable of determining different folding states of the same protein, or the binding events of proteins to ligands. Using the DeepDream algorithm, instances of key features of the deep neural network can be made visible in a range of images, allowing us to explore the inner workings of protein surface wave patter neural networks, as well as the creation of new images by finding and highlighting features of protein molecular spectra in a range of photographic input. The integration of the water-focused realization of cymatics, combined with neural networks and especially generative methods, offer a new direction to realize materiomusical Inceptionism as a possible direction in nano-inspired art. The method could have applications for detecting different protein structures, the effect of mutations, or uses in medical imaging and diagnostics, with broad impact in nano-to-macro transitions.
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low, we overcome this drawback by introducing a convolutional neural regression network for an automatic, outlier tolerant shape classification. From our experiments we expect two stable geometries: the so-called `slipper and `croissant shapes depending on the prevailing flow conditions and the cell-intrinsic parameters. Whereas croissants mostly occur at low shear rates, slippers evolve at higher flow velocities. With our method, we are able to find the transition point between both `phases of stable shapes which is of high interest to ensuing theoretical studies and numerical simulations. Using statistically based thresholds, from our data, we obtain so-called phase diagrams which are compared to manual evaluations. Prospectively, our concept allows us to perform objective analyses of measurements for a variety of flow conditions and to receive comparable results. Moreover, the proposed procedure enables unbiased studies on the influence of drugs on flow properties of single RBCs and the resulting macroscopic change of the flow behavior of whole blood.
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