We present a mathematical framework (referred to as Context-driven Actualization of Potential, or CAP) for describing how entities change over time under the influence of a context. The approach facilitates comparison of change of state of entities studied in different disciplines. Processes are seen to differ according to the degree of nondeterminism, and the degree to which they are sensitive to, internalize, and depend upon a particular context. Our analysis suggests that the dynamical evolution of a quantum entity described by the Schrodinger equation is not fundamentally different from change provoked by a measurement often referred to as collapse, but a limiting case, with only one way to collapse. The biological transition to coded replication is seen as a means of preserving structure in the fact of context-driven change, and sextual replication as a means of increasing potentiality thus enhancing diversity through interaction with context. The framework sheds light on concepts like selection and fitness, reveals how exceptional Darwinian evolution is as a means of change of state, and clarifies in what sense culture, and the creative process underlying it, are Darwinian.
The electrical properties of a set of seven-helix transmembrane proteins, whose space arrangement (3D structure) is known, are investigated by using regular arrays of the amino acids. These structures, specifically cubes, have topological features similar to those shown by the chosen proteins. The theoretical results show a good agreement between the predicted current-voltage characteristics obtained from a cubic array and those obtained from a detailed 3D structure. The agreement is confirmed by available experiments on bacteriorhodopsin. Furthermore, all the analyzed proteins are found to share the same critical behaviour of the voltage-dependent conductance and of its variance. In particular, the cubic arrangement evidences a short plateau of the excess conductance and its variance at high voltages. The results of the present investigation show the possibility to predict the I-V characteristics of multiple-protein sample even in the absence of a detailed knowledge of their 3D structure.
Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this paper, we calculated the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculated the mutual information, energy cost, and energy efficiency of an array of these bistable units. We found that the optimal number of units could maximize this arrays energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.
It is shown that the density of modes of the vibrational spectrum of globular proteins is universal, i.e., regardless of the protein in question it closely follows one universal curve. The present study, including 135 proteins analyzed with a full atomic empirical potential (CHARMM22) and using the full complement of all atoms Cartesian degrees of freedom, goes far beyond previous claims of universality, confirming that universality holds even in the high-frequency range (300- 4000 1/cm), where peaks and turns in the density of states are faithfully reproduced from one protein to the next. We also characterize fluctuations of the spectral density from the average, paving the way to a meaningful discussion of rare, unusual spectra and the structural reasons for the deviations in such outlier proteins. Since the method used for the derivation of the vibrational modes (potential energy formulation, set of degrees of freedom employed, etc.) has a dramatic effect on the spectral density, another significant implication of our findings is that the universality can provide an exquisite tool for assessing and improving the quality of various models used for NMA computations. Finally, we show that the input configuration too affects the density of modes, thus emphasizing the importance of simplified potential energy formulations that are minimized at the outset.
Cells are strongly out-of-equilibrium systems driven by continuous energy supply. They carry out many vital functions requiring active transport of various ingredients and organelles, some being small, others being large. The cytoskeleton, composed of three types of filaments, determines the shape of the cell and plays a role in cell motion. It also serves as a road network for the so-called cytoskeletal motors. These molecules can attach to a cytoskeletal filament, perform directed motion, possibly carrying along some cargo, and then detach. It is a central issue to understand how intracellular transport driven by molecular motors is regulated, in particular because its breakdown is one of the signatures of some neuronal diseases like the Alzheimer. We give a survey of the current knowledge on microtubule based intracellular transport. We first review some biological facts obtained from experiments, and present some modeling attempts based on cellular automata. We start with background knowledge on the original and variants of the TASEP (Totally Asymmetric Simple Exclusion Process), before turning to more application oriented models. After addressing microtubule based transport in general, with a focus on in vitro experiments, and on cooperative effects in the transportation of large cargos by multiple motors, we concentrate on axonal transport, because of its relevance for neuronal diseases. It is a challenge to understand how this transport is organized, given that it takes place in a confined environment and that several types of motors moving in opposite directions are involved. We review several features that could contribute to the efficiency of this transport, including the role of motor-motor interactions and of the dynamics of the underlying microtubule network. Finally, we discuss some still open questions.
The assembly of virus capsids from free coat proteins proceeds by a complicated cascade of association and dissociation steps, the great majority of which cannot be directly experimentally observed. This has made capsid assembly a rich field for computational models to attempt to fill the gaps in what is experimentally observable. Nonetheless, accurate simulation predictions depend on accurate models and there are substantial obstacles to model inference for such systems. Here, we describe progress in learning parameters for capsid assembly systems, particularly kinetic rate constants of coat-coat interactions, by computationally fitting simulations to experimental data. We previously developed an approach to learn rate parameters of coat-coat interactions by minimizing the deviation between real and simulated light scattering data monitoring bulk capsid assembly in vitro. This is a difficult data-fitting problem, however, because of the high computational cost of simulating assembly trajectories, the stochastic noise inherent to the models, and the limited and noisy data available for fitting. Here we show that a newer classes of methods, based on derivative-free optimization (DFO), can more quickly and precisely learn physical parameters from static light scattering data. We further explore how the advantages of the approaches might be affected by alternative data sources through simulation of a model of time-resolved mass spectrometry data, an alternative technology for monitoring bulk capsid assembly that can be expected to provide much richer data. The results show that advances in both the data and the algorithms can improve model inference, with rich data leading to high-quality fits for all methods, but DFO methods showing substantial advantages over less informative data sources better representative of the current experimental practice.
Self-propelled colloidal objects, such as motile bacteria or synthetic microswimmers, have microscopically irreversible individual dynamics - a feature they share with all living systems. The incoherent behaviour of individual swimmers can then be harnessed (or rectified) by microfluidic devices that create systematic motions impossible in equilibrium. Examples include flow of rotor particles round a circuit, steady rotation of a gear wheel in a bacterial bath, and pumping of bacteria between chambers by funnel gates. Here we present a computational proof-of-concept study, showing that such active rectification devices might be created directly from an unstructured primordial soup of motile particles, solely by using spatially modulated illumination to control their local propulsion speed. Alongside both microscopic irreversibility and speed modulation, our mechanism requires spatial symmetry breaking, such as a chevron light pattern, and strong interactions between particles, such as volume exclusion causing a collisional slow-down at high density. These four factors create a many-body rectification mechanism that generically differs from one-body microfluidic antecedents. Our work suggests that standard spatial-light-modulator technology might allow the programmable, light-induced self-assembly of active rectification devices from an unstructured particle bath.
Microscopic robots could perform tasks with high spatial precision, such as acting on precisely-targeted cells in biological tissues. Some tasks may benefit from robots that change shape, such as elongating to improve chemical gradient sensing or contracting to squeeze through narrow channels. This paper evaluates the energy dissipation for shape-changing (i.e., metamorphic) robots whose size is comparable to bacteria. Unlike larger robots, surface forces dominate the dissipation. Theoretical estimates indicate that the power likely to be available to the robots, as determined by previous studies, is sufficient to change shape fairly rapidly even in highly-viscous biological fluids. Achieving this performance will require significant improvements in manufacturing and material properties compared to current micromachines. Furthermore, optimally varying the speed of shape change only slightly reduces energy use compared to uniform speed, thereby simplifying robot controllers.
We study collections of self-propelled rods (SPR) moving in two dimensions for packing fractions less than or equal to 0.3. We find that in the thermodynamical limit the SPR undergo a phase transition between a disordered gas and a novel phase-separated system state. Interestingly, (global) orientational order patterns -- contrary to what has been suggested -- vanish in this limit. In the found novel state, the SPR self-organize into a highly dynamical, high-density, compact region - which we call aggregate - which is surrounded by a disordered gas. Active stresses build inside aggregates as result of the combined effect of local orientational order and active forces. This leads to the most distinctive feature of these aggregates: constant ejection of polar clusters of SPR. This novel phase-separated state represents a novel state of matter characterized by large fluctuations in volume and shape, related to mass ejection, and exhibits positional as well as orientational local order. SPR systems display new physics unseen in other active matter systems due to the coupling between density, active stresses, and orientational order (such coupling cannot be reduced simply to a coupling between speed and density).
Elastic network models (ENMs) are valuable and efficient tools for characterizing the collective internal dynamics of proteins based on the knowledge of their native structures. The increasing evidence that the biological functionality of RNAs is often linked to their innate internal motions, poses the question of whether ENM approaches can be successfully extended to this class of biomolecules. This issue is tackled here by considering various families of elastic networks of increasing complexity applied to a representative set of RNAs. The fluctuations predicted by the alternative ENMs are stringently validated by comparison against extensive molecular dynamics simulations and SHAPE experiments. We find that simulations and experimental data are systematically best reproduced by either an all-atom or a three-beads-per-nucleotide representation (sugar-base-phosphate), with the latter arguably providing the best balance of accuracy and computational complexity.
