Communications Physics

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.

The purpose of our studies is to elucidate the nature of massive control of whole genome expression with a particular emphasis on cell-fate change. Whole genome expression is coordinated by the emergence of a critical point (CP: a peculiar set of bi-phasic genes) through the genome-engine. In response to stimuli, the genome expression self-organizes three critical states, each exhibiting distinct collective behaviors with its center of mass acting as a local attractor, coexisting with whole genome attractor (GA). Genome-engine mechanism accounts for local attractors interaction in phase space. The CP acts as the organizing center of cell-fate change, and its activation makes local perturbation spread over the genome affecting GA. The activation of CP is in turn elicited by hot-spots, genes with elevated temporal variance, normally in charge to keep genome expression at pace with microenvironment fluctuations. When hot-spots oscillation exceeds a given threshold, the CP synchronizes with the GA driving genome expression state transition. The expression synchronization wave invading the entire genome depends on the power law fusion-bursting dynamics of silencing pericentromere-associated heterochromatin domains and the consequent folding-unfolding status of transcribing euchromatin domains. The proposed mechanism is a unified step toward a time-evolutional transition theory of biological regulation.

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.

In late 2022, China transitioned from a strict zero-COVID policy to rapidly abandoning nearly all interventions and data reporting. This raised great concern about the presumably-rapid but undisclosed spread of the SARS-CoV-2 Omicron variant in a very large population of very low pre-existing immunity. A quantitative understanding of the epidemic dynamics of COVID-19 during this period is urgently needed. Here, by modeling a combination of case count and survey data, we show that Omicron spread extremely fast, at a rate of 0.42/day (95% CrI: [0.35, 0.51]/day) after the full exit from zero-COVID policies on Dec. 7, 2022. Consequently, we estimate that the vast majority of the population (97%, 95% CrI [95%, 99%]) was infected during December, with the nation-wide epidemic peaking on Dec. 23. Overall, our results highlight the extremely high transmissibility of the variant and the importance of proper design of intervention exit strategies to avoid large infection waves.

Biocircuits often realize their functions only within specific parameter regimes, yet identifying those regimes and quantifying how difficult they are to satisfy remain challenging. Powered by the recently developed Reaction Order Polyhedra (ROP) framework enabling a holistic analysis of the behavior in biomolecular networks, we are now able to analyze these questions in a systematic way. In this work, we use ROP to derive the conditions under which Hill-like behaviors and adaptation in enzymatic negative feedback biocircuits can emerge. We also introduce the Realizability Index (R-index), i.e. the volume fraction of valid parameter regions, to quantify how hard it is for a biocircuit to achieve a desired function. We envision ROP theory and the R-index as important components of a new validity-aware conceptual language for studying and designing functional biocircuits.

Molecular motor-driven cargo must navigate a complex intracellular environment, which is often crowded, heterogeneous and fluctuating, to fulfill their diverse functions in a cell. To meet these challenges, most cargo display qualitatively similar transport behavior, that is, random switching between states of diffusive "jiggling" movement (off-state) and states of directed "runs" (on-state). The physical picture of this 2-state motion and their regulation in a cell are not well understood. Here, by using single-particle tracking and motion-states dissection, we present a statistical analysis of the 2-state motion of epidermal growth factor receptor (EGFR)-endosomes in living cells. From a thorough analysis of a large volume of EGFR-endosome trajectories, we reveal that their lifetime in both states feature an exponential distribution with its probability density function (PDF)-amplitude varied by more than three decades. We show that their characteristic time, on-state probability, velocity and off-state diffusion coefficient are spatially regulated, and are probably contributed by the endoplasmic reticulum (ER) network via its spatially varying membrane densities and interactions with the cargo. We further propose a 2-state transport model to describe the complex, spatially varying transport dynamics of EGFR-endosomes in a cell. Our findings suggest that the ER network may play an essential role in constructing a cellular-level free-energy landscape {Delta}G(r) to spatially guide cargo transport.

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.

Physiological functions of cells arise from the dynamics of chemical reaction networks. The cell cycle of fission yeast is controlled by dynamical changes in two cyclin-dependent kinase (CDK)-cyclin complexes based on a complicated reaction network consisting of protein synthesis, complex formation, and degradation1,2. Each of the two checkpoints, G1-S and G2-M, is driven by an increase in the concentration of CDK-Cig2 and CDK-Cdc13, respectively. However, it is not understood how these complexes in the single connected network are controlled independently in a stage-specific manner. Here we theoretically predict that independent control of CDK-Cdc13 from CDK-Cig2 is achieved by the topology of the cell cycle network, and experimentally validate this prediction, while updating the network information by comparing predictions and experiments. We analyzed a known cell cycle network using a topology-based theory3-6 and revealed that the two CDK-cyclin complexes are included in different "regulatory modules", suggesting that the concentration of each CDK-cyclin complex is controlled independently from the other. Experimental validation confirmed that the concentration of CDK-Cdc13 is controlled by the Cdc13 synthesis rate, independently from CDK-Cig2, as predicted. Conversely, the Cig2 synthesis rate affected not only CDK-Cig2 but also CDK-Cdc13. The fact, however, indicates the necessity of updating the network. We theoretically predicted the existence of an unknown necessary reaction, a Cdc13 degradation pathway, and experimentally confirmed it. The prediction and validation approach using the topology-based theory proposes a new systems biology, which progresses by comparing network structures with manipulation experiments and updating network information.

Cargo transport within cells is a vital biological process that relies on the intricate interplay between motor proteins, microtubules, and the complex intracellular environment. In this study, we unveil a universal transport mechanism characterized by stick-slip motion, which governs the dynamics of intracellular vesicle transport. By analyzing a comprehensive dataset of vesicle trajectories across various cell types and intracellular environments, we demonstrate that the cargo velocities consistently follow a Gamma distribution, revealing a common statistical pattern amidst the diversity of biological cargoes. Our experimental findings are well-described by a theoretical model that connects the Brownian-correlated kinetic friction between motor-cargo complexes and their surroundings to the observed universal Gamma distribution of cargo velocities. This model elucidates the stick-slip dynamics governing intracellular cargo transport, which are pertinent to various cellular processes such as vesicle budding, organelle transport, and cell migration.

This study presents the hypothesis that triosephosphate isomerase (TIM), a pivotal enzyme in glycolysis, functions as a quantum logic gate. Utilizing quantum mechanics, we model TIMs catalytic conversion of dihydroxyacetone phosphate (DHAP) to glyceraldehyde-3-phosphate (G3P) as a quantum operation involving precise proton transfer. To explore the broader implications of this quantum behavior, we developed a quantum model to assess the impact of Sodium-glucose co-transporter 2 inhibitors (SGLT2i) on methylglyoxal formation, a toxic byproduct linked to advanced glycation end products (AGEs). Our model predicts that SGLT2i could reduce methylglyoxal by decreasing the likelihood of intermediate formation, providing a potential mechanism for their protective effects observed in clinical contexts, including vascular and renal protection in diabetes, nephropathy, and heart failure. By reframing TIM as a quantum logic gate, this study not only challenges traditional views of enzymatic function but also opens new avenues for quantum biology, offering profound implications for the future of metabolic disease research and drug development. Moreover considering methylglyoxal as a result of a quantum tunnel inefficiency, its possible to hypothesize a new "noxa patogena" explicating its action as quantum interference.

We developed a mobility-informed disease-transmission model for COVID-19, inspired by collision theory in gas-phase chemistry. This simple kinetic model leads to a closed-form infectious population as a function of time and cumulative mobility. This model uses fatality data from Johns Hopkins to infer the infectious population in the past, and mobility data from Google, without social-distancing policy, geological or demographic inputs. It was found that the model appears to be valid for twenty hardest hit counties in the United States. Based on this model, the number of infected people grows (shrinks) exponentially once the relative mobility exceeds (falls below) a critical value ([~]30% for New York City and [~]60% for all other counties, relative to a median mobility from January 3 to February 6, 2020). A simple mobility cap can be used by government at different levels to control COVID-19 transmission in reopening or imposing another shutdown.

The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. Here, we propose a generalized SEIR model to analyze this epidemic. Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions. According to optimistic estimation, the epidemics in Beijing and Shanghai will end soon within two weeks, while for most part of China, including the majority of cities in Hubei province, the success of anti-epidemic will be no later than the middle of March. The situation in Wuhan is still very severe, at least based on public data until Feb. 15th. We expect it will end up at the beginning of April. Moreover, by inverse inference, we find the outbreak of COVID-19 in Mainland, Hubei province and Wuhan all can be dated back to the end of December 2019, and the doubling time is around two days at the early stage.

The question of whether physical phenomena at a quantum level significantly impact aspects of macroscopic life has long remained unanswered. Histone modification by acetylation regulates the transcriptional activity of genes, and thereby broadly impacts cellular metabolism. In chemical reactions, the quantum tunneling effect is a phenomenon in which a small quantum particle of the reactant can pass through the potential energy barrier, even if it does not have sufficient energy to overcome the barrier. Here, we demonstrated that quantum effects are involved in the enzymatic reaction of histone deacetylation, by monitoring kinetic isotope effects due to hydrogen isotopes of water molecules and their temperature dependence as indicators. Due to the kinetic isotope effects associated with the quantum effects, the reaction rate balance between histone acetylation and deacetylation in cells was altered with heavy water, which changed epigenetic transcription regulation in the cells. Thus, microscopic quantum mechanisms exist in histone deacetylation, thereby broadly impacting macroscopic life phenomena through epigenetic regulation. TeaserQuantum effects in enzymatic reaction of histone deacetylation latently influence life phenomena through epigenetic regulation.

The structural connectome serves as the foundation for neural information signaling, playing a primary constraint on brain functionality. Yet, its influence on emergent dynamical properties is not fully understood. Generally, a key measure of a systems structural impact on dynamical phenomena is its dimension. By tracking the temporal evolution of diffusive perturbations, we estimate a scale-dependent measure of dimension of empirical connectomes. At the local scale, it is highly heterogeneous and follows a gradient from sensory-motor to executive areas. At the global scale, it encapsulates mesoscale topological information related to the balance between segregation and integration. Furthermore, by comparing connectomes from stroke patients, we find that dimension captures the local effects of lesions and, at a global level, is linked to impaired critical patterns and decreased cognitive performance. Overall, the dimension of the connectome may serve as a powerful descriptor for bridging the gap between structure and function in the human brain.

The successful integration of engineered gene circuits into host cells remains a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback, where the circuit influences cell growth and vice versa. Understanding the dynamics of circuit failures and identifying topologies resilient to growth feedback are crucial for both fundamental and applied research. Utilizing transcriptional regulation circuits with adaptation as a paradigm, we systematically study more than four hundred topological structures and uncover various categories of failures. Three dynamical mechanisms of circuit failures are identified: continuous deformation of the response curve, strengthened or induced oscillations, and sudden switching to coexisting attractors. Our extensive computations also uncover a scaling law between a circuit robustness measure and the strength of growth feedback. Despite the negative effects of growth feedback on the majority of circuit topologies, we identify several circuits that maintain optimal performance as designed, a feature important for applications.

Understanding why specific metabolic states become stable in cancer has remained a fundamental challenge, as current pathway-centric frameworks lack a unifying physical principle governing global metabolic organization. We introduce the Metabolic Spin-Glass (MSG) model, which recasts cellular metabolism as a frustrated many-body system governed by a Hamiltonian that integrates reaction free energies, cofactor-mediated thermodynamic couplings, and patient-specific transcriptomic fields. The Hamiltonian is formulated as a binary optimization problem and solved using hybrid quantum annealing. Embedding gastric cancer transcriptomes (n=497) reveals that malignant phenotypes correspond to thermodynamically distinct ground states rather than isolated pathway perturbations. The Warburg effect emerges intrinsically as a thermodynamic phase transition, and stem-like tumors occupy the deepest attractor basin reflecting high energetic stability. A thermodynamic order parameter stratifies patients into prognostically distinct subtypes independently of transcriptomic classification, suggesting clinically applicable non-redundant biomarkers. This work establishes a spin-glass energy landscape framework for physically principled, patient-specific cancer metabolic stratification.

Bacterial quorum sensing (QS) is a cell-to-cell communication mechanism through which bacteria share information about cell density, and tune gene expression accordingly. Pathogens exploit QS to orchestrate virulence and regulate the expression of genes related to antimicrobial resistance. Despite the vast literature on QS, the properties of the underlying molecular network are not entirely clear. We compare two synthetic QS circuit architectures: in the first, a single positive feedback loop autoinduces the synthesis of the signal molecule; the second includes an additional positive feedback loop enhancing the synthesis of the signal molecule receptor. Our comprehensive analysis of the two systems and their equilibria highlights the differences in the bistable and hysteretic behaviors of the alternative QS structures. Finally, we investigate three different QS inhibition approaches; numerical analysis predicts their effect on the steady-state behavior of the two different QS models, revealing critical parameter thresholds that guarantee an effective QS suppression.

Phenotype transition takes place in many biological processes such as differentiation and reprogramming. A fundamental question is how cells coordinate switching of expressions of clusters of genes. Through analyzing single cell RNA sequencing data in the framework of transition path theory, we studied how such a genome-wide expression program switching proceeds in five different cell transition processes. For each process we reconstructed a reaction coordinate describing the transition progression, and inferred the gene regulation network (GRN) along the reaction coordinate. In all processes we observed common pattern that the overall effective number and strength of regulation between different communities increase first and then decrease. The change accompanies with similar change of the GRN frustration, defined as overall conflict between the regulation received by genes and their expression states, and GRN heterogeneity. While studies suggest that biological networks are modularized to contain perturbation effects locally, our analyses reveal a general principle that during a cell phenotypic transition, intercommunity interactions increase to concertedly coordinate global gene expression reprogramming, and canalize to specific cell phenotype as Waddington visioned.

The analysis of large gene and metabolic networks is often hindered by unknown biochemical parameters and the nonlinear nature of classical S-system models. To address this, we introduce a framework based on combinatorial toric geometry computed with tools such as Normaliz, SageMath, it is worth mentioning this technique in not restrictive to integer vectors, there exists a natural extension to real geometries. Unlike traditional approaches, which rely on parameter dependent fixed points, our method constructs a Topological Environment derived from the dual space of kinetic orders, leading to what we call orthogonal enzyme kinetics. Within this topological setting, fixed points are computed on the algebraic torus, enabling the transformation of nonlinear dynamics into linear forms. Importantly, these fixed points are independent of kinetic parameters and depend only on network topology and interaction signs. Applying this methodology to gene circuits involved in circadian rhythms, we reproduce previously reported oscillatory physiologies.

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.