Communications Physics

We investigate how individual protective behaviors, different levels of testing, and isolation influence the transmission and control of the COVID-19 pandemic. Based on an SEIR-type model incorporating asymptomatic but infectious individuals (40%), we show that the pandemic may be readily controllable through a combination of testing, treatment if necessary, and self-isolation after testing positive (TTI) of symptomatic individuals together with social protection (e.g., facemask use, handwashing). When the basic reproduction number, R0, is 2.4, 65% effective social protection alone (35% of the unprotected transmission) brings the R below 1. Alternatively, 20% effective social protection brings the reproduction number below 1.0 so long as 75% of the symptomatic population is covered by TTI within 12 hours of symptom onset. Even with 20% effective social protection, TTI of 1 in 4 symptomatic individuals can substantially 'flatten the curve' cutting the peak daily incidence in half.

We show that the COVID-19 pandemic under social distancing exhibits universal dynamics. The cumulative numbers of both infections and deaths quickly cross over from exponential growth at early times to a longer period of power law growth, before eventually slowing. In agreement with a recent statistical forecasting model by the IHME, we show that this dynamics is well described by the erf function. Using this functional form, we perform a data collapse across countries and US states with very different population characteristics and social distancing policies, confirming the universal behavior of the COVID-19 outbreak. We show that the predictive power of statistical models is limited until a few days before curves flatten, forecast deaths and infections assuming current policies continue and compare our predictions to the IHME models. We present simulations showing this universal dynamics is consistent with disease transmission on scale-free networks and random networks with non-Markovian transmission dynamics.

Most countries are suffering severely from the ongoing covid-19 pandemic despite various levels of preventive measures. A common question is if and when a country or region will reach herd immunity h. The classical herd immunity level hC is defined as hC =1-1/R0, where R0 is the basic reproduction number, for covid-19 estimated to lie somewhere in the range 2.2-3.5 depending on country and region. It is shown here that the disease-induced herd immunity level hD, after an outbreak has taken place in a country/region with a set of preventive measures put in place, is actually substantially smaller than hC. As an illustration we show that if R0 =2.5 in an age-structured community with mixing rates fitted to social activity studies, and also categorizing individuals into three categories: low active, average active and high active, and where preventive measures affect all mixing rates proportionally, then the disease-induced herd immunity level is hD = 43% rather than hC =1-1/2.5 = 60%. Consequently, a lower fraction infected is required for herd immunity to appear. The underlying reason is that when immunity is induced by disease spreading, the proportion infected in groups with high contact rates is greater than that in groups with low contact rates. Consequently, disease-induced immunity is stronger than when immunity is uniformly distributed in the community as in the classical herd immunity level.

The collective spatial phenomenon of complement protein opsonization on nanoparticle surfaces is a key component of the immune response to viruses, engineered nanoparticles, and diseased cells. Recent work showed this opsonization follows a sharp, percolation-like transition versus the spacing d between surface-bound attachment sites, leaving two open questions: 1) whether the transition exhibits hallmarks of true criticality, such as diverging susceptibility, and 2) whether it can be distinguished from an alternative first-order cooperative (Hill-type) process producing an equally sharp threshold without true criticality. Here, we resolve both questions using a hierarchical statistical-mechanics treatment spanning stochastic, mean-field, and spatial reaction-diffusion models. The variance of two order parameters, peak complement activity and activation lifetime, diverges near threshold and sharpens systematically with system size, the defining signature of a critical point rather than a smooth cooperative response. Extending the analysis across site spacing and intrinsic kinetic rate constants traces a two-dimensional locus of critical points with consistent critical exponents throughout, establishing a single, robust universality class. The mean-field dynamic exponent for activation lifetime agrees quantitatively with the exact value predicted for the general epidemic process. Finally, a reaction-diffusion model of the nanoparticle surface shows the critical locus is set by a diffusion-limited length scale, establishing complement percolation as a fundamentally transport-limited surface reaction. These results place complement activation within the percolation universality class and identify the physical parameters, diffusion, catalysis, and decay, that govern its critical threshold, with direct implications for rational design of complement-evading nanomaterials, immunology, and evolutionary biology.

Cellular processes, in particular homeostasis and growth, require an intricate and complex exchange of matter between a cell and its surroundings. Yet experimental difficulties have prevented a detailed description of the dynamics of a cells mass and volume along different cellular processes, limiting our understanding of cell physiology in health and disease. It has been recently observed that single mammalian cells fluctuate their mass in a timescale of seconds. This result challenges central and long-standing cell growth models, according to which cells increase their mass either linearly or exponentially throughout the cell cycle. However, it remains unclear to what extent cell mass fluctuations may be sustained in multicellular organisms. Here I provide a mathematical model for cell mass fluctuations and explore how such fluctuations can be successfully sustained in multicellular organisms. I postulate that cells do not synchronise their mass fluctuations, but they are executed with their phases uniformly distributed. I derive a mathematical expression to estimate the resulting mass shift between fluid compartments in an organism due to cell mass fluctuations. Together with a new estimate of 4x1013 human cells in the body, I demonstrate that my hypothesis leads to shifts of mass between the intracellular and extracellular fluid compartments in the human body that are approximately or smaller than 0.25 mg and, therefore, perfectly viable. The proposed model connects cell physiology with information theory and entropy.

Accurate signal processing is essential for proper cell functions, and can be achieved through kinetic proofreading, where an enzyme undergoes sequential state transitions and irreversible deactivation to enable high fidelity. However, synthetically constructing a biological proofreading system has been hindered by the difficulty in engineering single molecular state transitions. Here, we designed a protein circuit that combines diffusion and endocytosis to enable kinetic proofreading at the multimolecular level, without the conservation of total enzymes implicitly assumed in classic kinetic proofreading. Simulations revealed a previously overlooked yet experimentally crucial trade-off between circuit activity and fidelity, and theoretical analysis confirmed it to be fundamental in all kinetic proofreading systems. By integrating self-activation and mutual inhibition mechanisms, the circuit overcomes this activity-fidelity trade-off within biologically plausible parameter regimes. Our results extend proofreading schemes from single enzymes to a multimolecular context, and represent a practical and generalizable strategy for constructing high-fidelity synthetic biological circuits. HIGHLIGHTSO_LIWe design a multimolecular and multicellular proofreading circuit C_LIO_LIA previously overlooked yet practically relevant trade-off arises between circuit activity and fidelity C_LIO_LIThe activity-fidelity trade-off is fundamental in all kinetic proofreading circuits C_LIO_LISelf-activation and mutual inhibition mechanisms collectively overcome the activity-fidelity trade-off C_LI

The complex spatiotemporal flow patterns in living tissues, driven by active forces, have many of the characteristics associated with inertial turbulence even though the Reynolds number is extremely low. Analyses of experimental data from two-dimensional epithelial monolayers in combination with agent-based simulations show that cell division and apoptosis lead to directed cell motion for hours, resulting in rapid topological transitions in neighboring cells. These transitions in turn generate both long ranged and long lived clockwise and anticlockwise vortices, which gives rise to turbulent-like flows. Both experiments and simulations show that at long wavelengths the wave vector (k) dependent energy spectrum E(k) {approx} k-5/3, coinciding with the Kolmogorov scaling in fully developed inertial turbulence. Using theoretical arguments and simulations, we show that long-lived vortices lead to long-time tails in the velocity auto-correlation function, Cv(t) [~] t-1/2, which has the same structure as in classical 2D fluids but with a different scaling exponent.

Understanding the molecular residence autocorrelation function in liquid is of fundamental importance in physical and life science. Encoded in this function is not only the binding properties, but also the information of the liquid environment. Based on extensive in silico experiments and theoretical analysis, we reveal that power law residence scaling arises in both passive and active liquid, in contrast to the common sense of exponential decay. In simple homogeneous liquid, the scaling exponent depends solely on the system dimensionality. Such scaling law is robust against the superposition of diverse binding energies in single-phase liquid but can be breached if the system undergoes phase separation. Remarkably, in a dissipative system where phase separation is subject to non-equilibrium feedback controls, an anomalous power law emerges whose scaling exponent is in line with the puzzling residence scaling of transcription factors reported in recent experiments. Our results highlight the sensitivity of molecular residence to its surrounding liquid and suggest that active phase separation can serve as a scaling proofreading mechanism in gene regulation.

A distinct feature of living systems is their capacity to take decisions based on uncertain environmental signals. Examples span from the microscopic scale of cell differentiation guided by the concentration of a morphogen, to complex choices made by animals and humans. The current paradigm in decision theory assumes that decisions, once taken, cannot be revoked. However, living systems often amend their decisions if new evidence favors an alternative hypothesis. In this paper, we characterize the optimal strategy for such amendable decisions. We find that, unlike irrevocable decisions, optimal amendable decisions can be made in a finite average time with zero error probability. Our theory successfully predicts the outcome of a visual experiment involving human participants and the accuracy of cell-fate decisions in early development. Our study reveals that amendments lead to a substantial advantage in decision-making, that is likely to be widely exploited by living beings.

The Landauers principle sets a thermodynamic bound of kBT ln 2 on the energetic cost of erasing each bit of information. It holds for any memory device, regardless of its physical implementation. It was recently shown that carefully built artificial devices can saturate this bound. In contrast, biological computation-like processes, e.g., DNA replication, transcription and translation use an order of magnitude more than their Landauers minimum. Here we show that saturating the Landauer bound is nevertheless possible with biological devices. This is done using a mechanosensitive channel of small conductance (MscS) from E. coli as a memory bit. MscS is a fast-acting osmolyte release valve adjusting turgor pressure inside the cell. Our patch-clamp experiments and data analysis demonstrate that under a slow switching regime, the heat dissipation in the course of tension-driven gating transitions in MscS closely approaches its Landauers limit. We discuss the biological implications of this physical trait.

Chemical reactions are responsible for information processing in living cells, and thermodynamic trade-off relations can explain their accuracy and speed. Its experimental test in living cells had not existed despite its importance because it is hard to justify sample size sufficiency. This paper reports the first experimental test of the thermodynamic trade-off relation, namely the thermodynamic speed limit, in living systems at the single-cell level where the sample size is relatively small. Due to the information-geometric approach, we can demonstrate the thermodynamic speed limit for the extracellular signal-regulated kinase phosphorylation using time-series fluorescence imaging data. Our approach quantifies the intrinsic speed of cell proliferation and can potentially apply other signal transduction pathways to detect their information processing speed. One-Sentence SummaryExperimental measurement of information thermodynamic speed by fluorescence imaging in living cells

The recent outbreak of the Covid-19 suggests a rather long latent phase that precludes public health officials to predict the pandemic transmission on time. Here we apply mass action laws and chemical transition theory to propose a kinetic model that accounts for viral transmission dynamics at the latent phase. This model is useful for authorities to make early preventions and control measurements that stop the spread of a deadly new virus.

Living systems exhibit extraordinary resilience, adaptability, and identity preservation despite continuous atomic turnover. Traditional physics explains this persistence through biochemical stability, but a deeper quantuminformational description remains elusive. Here, we introduce a theoretical framework where a sustaining superoperator ([S]) exactly cancels environmental decoherence ([D]) within the Lindblad formalism, maintaining quantum coherence indefinitely. The resulting sustained pure-state system exhibits vanishing entropy production, stable informational identity, and finite tunneling amplitude under sublinear effective-mass scaling (Meff = m Na) with (0 < < 1). Numerical simulations confirm entropy cancellation, identity invariance under substrate replacement, and anomalous tunneling consistent with coherence-preserving collectivity. These findings propose mathematically consistent conditions for substrate-independent identity persistence and coherent embodiment, connecting concepts from quantum biology, information theory, and open-system thermodynamics.

Enzymatic reactions in biomolecular condensates are often assumed to be regulated through local enrichment of reactants. However, condensates also reshape molecular transport and reaction kinetics, making it unclear how phase separation controls catalysis in living cells. Here, we develop a quantitative theory of biomolecular catalysis in phase-separated systems and find that liquid condensates can act as tunable catalytic switches, transitioning between regimes of enhanced and suppressed enzymatic activity, exhibiting optimal responses at biologically relevant condensate sizes. We show that condensate-mediated catalysis cannot be understood from reactant enrichment alone, but instead emerges from the coupled interplay of molecular partitioning, diffusive transport, and phase-dependent reaction kinetics. The strongest regulatory effects occur under rapid interphase exchange, where the spatially heterogeneous catalytic network admits a system-level Michaelis-Menten description governed by system-averaged concentrations and reaction kinetics. Our framework predicts that micron-sized condensates can either enhance or suppress enzymatic activity by up to two orders of magnitude, and that optimal catalytic regulation can emerge at condensate sizes comparable to many biomolecular condensates. These results provide experimentally testable predictions for condensate-mediated catalysis and establish quantitative principles for understanding and engineering enzyme-catalysed reactions in biomolecular condensates.

Species interactions--ranging from direct predator-prey relationships to indirect effects mediated by the environment--are central to ecosystem balance and biodiversity. While empirical methods for measuring these interactions exist, their interpretability and limitations remain unclear. Here we examine the empirical matrix of pairwise interactions, a widely used tool, and analyze its temporal variability. We show that apparent fluctuations in interaction strength--and even shifts in interaction signs, often interpreted as transitions between competition and facilitation--can arise intrinsically from population dynamics with fixed ecological roles. Experimental protocols further shape these estimates: the duration of observation and the type of setup in microbial growth studies (e.g., chemostats, batch cultures, or resource conditions) systematically affect measured interactions. Considering interactions across timescales enhances interpretability: short-term measurements primarily capture direct species couplings, whereas long-term observations increasingly reflect indirect community feedback. Taken together, these results establish short-duration inferences, obtained either directly or extrapolated, as a principled way to disentangle direct from indirect interactions. Building on this insight, we propose a model-inference approach that leverages multiple short time series rather than extended longitudinal datasets.

Model reduction is the construction of simple yet predictive descriptions of the dynamics of many-body systems in terms of a few relevant variables. A prerequisite to model reduction is the identification of these relevant variables, a task for which no general method exists. Here, we develop a systematic approach based on the information bottleneck to identify the relevant variables, defined as those most predictive of the future. We elucidate analytically the relation between these relevant variables and the eigenfunctions of the transfer operator describing the dynamics. Further, we show that in the limit of high compression, the relevant variables are directly determined by the slowest-decaying eigenfunctions. Our information-based approach indicates when to optimally stop increasing the complexity of the reduced model. Furthermore, it provides a firm foundation to construct interpretable deep learning tools that perform model reduction. We illustrate how these tools work in practice by considering uncurated videos of atmospheric flows from which our algorithms automatically extract the dominant slow collective variables, as well as experimental videos of cyanobacteria colonies in which we discover an emergent synchronization order parameter. Significance StatementThe first step to understand natural phenomena is to intuit which variables best describe them. An ambitious goal of artificial intelligence is to automate this process. Here, we develop a framework to identify these relevant variables directly from complex datasets. Very much like MP3 compression is about retaining information that matters most to the human ear, our approach is about keeping information that matters most to predict the future. We formalize this insight mathematically and systematically answer the question of when to stop increasing the complexity of minimal models. We illustrate how interpretable deep learning tools built on these ideas reveal emergent collective variables in settings ranging from satellite recordings of atmospheric fluid flows to experimental videos of cyanobacteria colonies.

Biomolecular condensates have been proposed to buffer intracellular concentrations and reduce noise. Recent results demonstrate that concentrations need not be buffered in multicomponent systems, leading to a non-constant saturation concentration ( csat ) when individual components are varied. Simplified equilibrium considerations suggest that noise reduction might be closely related to concentration buffering and that a fixed saturation concentration is required for noise reduction to be effective. Here we present a theoretical analysis to demonstrate that these suggestions do not apply to mesoscopic fluctuating systems. We show that concentration buffering and noise reduction are distinct concepts, which cannot be used interchangeably. We further demonstrate that concentration buffering - and a constant csat - are neither necessary nor sufficient for noise reduction to be effective. Clarity about these concepts is important for studying the role of condensates in controlling cellular noise and for the interpretation of concentration relationships in cells.

How cells translate growth factor (GF) signals into context-specific phenotypes remains a fundamental question in cell biology. The classical view holds that cells translate a constant concentration of a GF with specific chemical identity to a steady state activation of the underlying signaling pathway, resulting in a defined phenotypic response. However, recent findings suggest that even a single GF, when presented in a pulsatile manner, drives differential phenotypic responses depending on the frequency of stimulation. To reconcile these views, we introduce a novel conceptual framework of "signaling homeorhesis". Unlike homeostasis, homeorhesis describes the stable evolution of signaling trajectories over time. Defining this concept quantitatively using a dynamical systems framework, we use mathematical models of the Epidermal Factor Growth Receptor (EGFR) and the Tropomyosin receptor kinase A (TrkA) networks, as well as available experimental temporal protein activity recordings in PC-12 cells to demonstrate that cells classify GF signals to unique signaling trajectories that encode for distinct cell phenotypes, irrespective of the GF identity. We thereby propose that the cellular phenotype is determined in real-time, as the cell actively interprets the growth factor signals from its environment.

Cellular adaptation is inherently nonstationary processes with complex stochastic dynamics1-5. Despite remarkable progress in quantitative biology6-11, a quantitative understanding of the cell adaptation dynamics in terms of the underlying cellular network remains elusive. Here, we present the next-generation chemical dynamics model and theory for cellular networks, providing an effective, quantitative description of the adaptive gene expression dynamics in living cells responding to external stimuli. Unlike conventional kinetics, chemical dynamics of cellular network modules are characterized by their reaction-time distributions, rather than by rate coefficients12. For a general model of cell signal transduction and adaptive gene expression, we derive exact analytical expressions for the time-dependent mean and variance of protein numbers produced in response to external stimuli, validated by accurate stochastic simulations. These results provide a unified, quantitative explanation of the stochastic responses of diverse E. coli genes to antibiotic stress and transcriptional induction. Our analysis reveals existence of a general quadratic relationship between the mean and variance of activation times across diverse genes. The gene activation process influences transient dynamics of downstream protein levels, but not their steady-state levels. In contrast, post-translational maturation process affects both transient dynamics and steady-state variability of mature protein levels. This finding indicates that the gene expression variability measured by fluorescent reporter proteins depends on the maturation time of the reporters. This work suggests a new direction for the development of digital twins of living cells.

Structural network representations of metastatic dissemination typically focus on static topology without resolving transport dynamics, relaxation timescales, or steady-state behaviour. Here, we formulate a discrete Markovian transport model on a directed higher-order network with transition rates derived from qualitative clinical affinity classes. By constructing a non-Hermitian row-stochastic transfer operator, we characterise the relaxation dynamics through its spectral decomposition. The system exhibits a fast-mixing regime characterised by a spectral gap of {gamma} {approx} 0.67, corresponding to a characteristic relaxation timescale of {tau} {approx} 1.49 discrete steps, with the influence of the primary tumour origin progressively attenuated during dissemination. Convergence towards a non-equilibrium steady state (NESS) is accompanied by a reduction in Shannon entropy, concentrating probability mass within specific topological sinks. This spectral relaxation delineates two distinct dynamical regimes: early transient dissemination (n < {tau}), dominated by local organ-specific transition probabilities (organotropism), and the asymptotic regime (n > {tau}), determined increasingly by the global transport architecture of the network. Comparison with independent clinical and autopsy observations across 21 primary tumours and 23 target organs indicates that the predicted stationary distribution is consistent with the observed hierarchy of metastatic organ involvement.