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Weak Galilean Invariance As a Selection Principle for Coarse Weak Galilean invariance as a selection principle for coarse-grained diffusive models Andrea Cairolia,b, Rainer Klagesb, and Adrian Bauleb,1 aDepartment of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom; and bSchool of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom Edited by David A. Weitz, Harvard University, Cambridge, MA, and approved April 16, 2018 (received for review October 2, 2017) How does the mathematical description of a system change in dif- in the long-time limit, hX 2i ∼ t β with β = 1, for anomalous dif- ferent reference frames? Galilei first addressed this fundamental fusion it scales nonlinearly with β 6=1. Anomalous dynamics has question by formulating the famous principle of Galilean invari- been observed experimentally for a wide range of physical pro- ance. It prescribes that the equations of motion of closed systems cesses like particle transport in plasmas, molecular diffusion in remain the same in different inertial frames related by Galilean nanopores, and charge transport in amorphous semiconductors transformations, thus imposing strong constraints on the dynam- (5–7) that was first theoretically described in refs. 8 and 9 based ical rules. However, real world systems are often described by on the continuous-time random walk (CTRW) (10). Likewise, coarse-grained models integrating complex internal and exter- anomalous diffusion was later found for biological motion (11– nal interactions indistinguishably as friction and stochastic forces. 13) and even human movement (14). Recently, it was established Since Galilean invariance is then violated, there is seemingly no as a ubiquitous characteristic of cellular processes on a molec- alternative principle to assess a priori the physical consistency of a ular level (15). Here, anomalous diffusion is observed, e.g., in given stochastic model in different inertial frames. Here, starting neuronal messenger ribonucleoprotein transport (16); in protein from the Kac–Zwanzig Hamiltonian model generating Brownian structural fluctuations (17); and in the intracellular transport of motion, we show how Galilean invariance is broken during the Saccharomyces cerevisiae mitochondria (18), chromosomal loci coarse-graining procedure when deriving stochastic equations. of Escherichia coli cells (19, 20), engulfed microspheres (21), Our analysis leads to a set of rules characterizing systems in differ- and lipid and insulin granules (22, 23). However, because of PHYSICS ent inertial frames that have to be satisfied by general stochastic the intrinsic difficulties in assessing the details of the micro- models, which we call “weak Galilean invariance.” Several well- scopic interactions in experiments, theoretical models for such known stochastic processes are invariant in these terms, except anomalous processes cannot be typically derived from first prin- the continuous-time random walk for which we derive the cor- ciples and are usually formulated on mostly phenomenological rect invariant description. Our results are particularly relevant for grounds. In fact, a wealth of diffusive models has been suggested the modeling of biological systems, as they provide a theoretical in the literature, which rely on spatiotemporal memory effects principle to select physically consistent stochastic models before a and non-Gaussian power-law statistics of various observables (5, validation against experimental data. 7, 24, 25). Unfortunately so far there is no fundamental rule available that could be used to verify the physical consistency of stochastic processes j Galilean invariance j anomalous transport j such stochastic models a priori. To distinguish between different coarse-graining j fractional calculus models it remains only the comparison with experimental data that is often imprecise due to limited sample sizes. lassical mechanics is built upon the two intimately related Cconcepts of inertial reference frames and Galilean invari- Significance ance (GI) (1). The former are coordinate systems where a freely moving particle (i.e., in the absence of external forces) either is Galilean invariance is a cornerstone of classical mechanics. It at rest or exhibits uniform rectilinear motion. The latter prin- states that for closed systems the equations of motion of ciple states that in different inertial frames the equations of the microscopic degrees of freedom do not change under motion of closed systems, i.e., including all their interacting con- Galilean transformations to different inertial frames. How- stituents, are invariant with respect to Galilean transformations ever, the description of real world systems usually requires (GTs). These are in general affine transformations that pre- coarse-grained models integrating complex microscopic inter- serve both time intervals and distances between simultaneous actions indistinguishably as friction and stochastic forces, events (1). For systems whose dynamical evolution can be fully which intrinsically violate Galilean invariance. By studying the characterized by microscopic deterministic models, GI plays a coarse-graining procedure in different frames, we show that fundamental constitutive role, manifest in the constraints that alternative rules—denoted as “weak Galilean invariance”— it naturally imposes on the functional form of Newton’s equa- need to be satisfied by these stochastic models. Our results tion. However, a large variety of complex systems in science and highlight that diffusive models in general cannot be cho- nature are not modeled on a microscopic level with Newtonian sen arbitrarily based on the agreement with data alone equations of motion, but rather on a mesoscopic level using, but have to satisfy weak Galilean invariance for physical e.g., stochastic Langevin equations or Fokker–Planck diffusion consistency. equations to capture the coarse-grained effects of microscopic interactions as friction and noise on the relevant degrees of free- Author contributions: A.C., R.K., and A.B. designed research; A.C. performed research; dom. The applications of such equations and their variants are A.C., R.K., and A.B. analyzed data; and A.C., R.K., and A.B. wrote the paper. vast throughout the sciences (2–4). The authors declare no conflict of interest. Coarse-grained diffusive models are particularly relevant to This article is a PNAS Direct Submission. describe anomalous transport phenomena, where stochasticity Published under the PNAS license. arises due to complex multiparticle interactions, whose precise 1 To whom correspondence should be addressed. Email: [email protected]. form is usually unknown. While for normal diffusion due to This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. Brownian motion the mean-square displacement (MSD) of an 1073/pnas.1717292115/-/DCSupplemental. ensemble of particles with positions X (t) at time t grows linearly www.pnas.org/cgi/doi/10.1073/pnas.1717292115 PNAS Latest Articles j 1 of 6 Downloaded by guest on September 27, 2021 @ X Here, we show that GI can provide precisely such a constitu- x_i (t) = vi (t), mi v_i (t) = − U (xi (t), xj (t)): [3] @xi tive principle. Even though the fundamental role of GI seemingly i<j breaks down for stochastic diffusive models due to the presence of friction (26), they are nevertheless constrained by a weak form of GI to be physically consistent in different inertial frames. The Transforming the coordinates to the reference frame Se via Eq. _ _ @ P 1, we see that x i (t) = vi (t) and mi v i (t) = − U (xi (t), weak GI rules derived below thus represent a general selection e e e @xei i<j e principle for stochastic coarse-grained models. Previously, the xej (t)) if U depends only on the relative difference between consequences of GI in the context of statistical mechanics were the particles’ positions, i.e., U (xi (t), xj (t)) = U (xi (t) − xj (t)), first explored for fluid dynamics, where it establishes specific because in this case xei (t) − xej (t) = xi (t) − xj (t). We thus relations between critical exponents of the characteristic param- recover Newton’s equations of motion satisfying his third law, eters entering the derivation of the Navier–Stokes equation (27) which are identical in both reference frames; i.e., they satisfy GI. [although this result has been challenged (28)]. The problem Our goal is now to derive coarse-grained dynamics from systems carries over to the famous Kardar-Parisi-Zhang equation (29) described by Eq. 3, where some of the microscopic degrees of whose GI is equally debated (30). Whether or not these statis- freedom have been eliminated, and to characterize their statistics tical equations feature GI has important practical implications on such a mesoscopic level in both frames S, Se (Fig. 1). for the modeling of, e.g., fluid flows (28) and nonlinear biologi- The transition from Eq. 3 to an effective description in the cal growth (31). Specifically, in molecular dynamics simulations form of a stochastic diffusion equation can be made quantita- of fluids using stochastic Langevin thermostats it was found tively precise for the specific scenario where one of the particles, that Langevin dynamics break GI by violating global momentum for simplicity let it be the (N + 1)th, is a tagged (tracer) particle conservation, which makes it unsuitable to simulate hydrody- of mass mN +1 = M that interacts with the remaining particles namic phenomena (32). Curing this deficiency led to novel of equal mass mj = m via a harmonic potential of coupling 2 GI
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