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We study the dynamics of proliferating cell collectives whose microscopic constituents' growth is inhibited by macroscopic growth-induced stress. Discrete particle simulations of a growing collective show the emergence of concentric-ring patterns in cell size whose spatio-temporal structure is closely tied to the individual cell's stress response. Motivated by these observations, we derive a multiscale continuum theory whose parameters map directly to the discrete model. Analytical solutions of this theory show the concentric patterns arise from anisotropically accumulated resistance to growth over many cell cycles. This work shows how purely mechanical processes can affect the internal patterning and morphology of cell collectives, and provides a concise theoretical framework for connecting the micro- to macroscopic dynamics of proliferating matter.

We investigate a novel scenario involving asymmetric keV-range dark matter (DM) in the form of right-handed (sterile) neutrinos. Based on the Fermi-Dirac distribution, we demonstrate that asymmetric fermionic DM forms a Fermi degenerate gas, making it potentially colder than symmetric fermionic DM. This setup simultaneously accounts for the Universe's baryon asymmetry through tiny Yukawa interactions with Standard Model leptons and the Higgs field, and the homochirality of amino acids via decay into circularly polarized photons. This scenario can be investigated through soft X-ray searches conducted by current and upcoming space missions. The helical X-rays is a smoking-gun signal of our scenario. Additionally, we propose a new mechanism to suppress DM thermal production by introducing a light modulus, which may also benefit cosmology involving generic right-handed neutrinos with large mixing., Comment: 22pages, 3figures, comments are welcome

The emerging technique of serial femtosecond X-ray crystallography (SFX) can be used to study the structure and dynamics of biological macromolecules to high spatial and temporal resolutions. An ongoing challenge for SFX is the damage caused by the ultrabright X-ray free electron laser pulse. Though it is often assumed that sufficiently femtosecond pulses `outrun' radiation damage, in reality electronic damage processes commence during exposure. We model the electronic damage to protein nanocrystals using a plasma model that tracks the continuous changes to the energy distribution of the unbound electrons. Tracking the continuous energy distribution is of particular importance for distinguishing the influence of differing elements on secondary damage processes. Heavy atoms have a ubiquitous but small presence in protein targets - typically as integral components of the macromolecule and as salts in the solvent. We find that these atoms considerably influence the simulated ionization and scattering behavior of realistic targets due to their rapid seeding of secondary ionization processes. In lysozyme, even the presence of native sulfur atoms significantly contributes to theoretical measures of damage-induced noise for >= 6 keV, 15 fs pulses. Contributing to the effect is that heavy atoms seed `intermediate' energy electron cascades that are particularly effective at ionizing the target on the femtosecond timescale. In addition, the disproportionate effect of heavy atoms means the damage to a protein crystal can be sensitive to their presence in the solvent. Outside of reducing the concentration of heavy atoms in the target, these results suggest the dose limits of SFX targets will be higher where the ionization of deep >~ 6 keV absorption edges is minimized, or, to a lesser extent, when such edges are only ionized with X-rays >> 2 keV above their binding energy.

Synchronization plays a crucial role in the dynamics of living organisms, from fireflies flashing in unison to pacemaker cells that jointly generate heartbeats. Uncovering the mechanism behind these phenomena requires an understanding of individual biological oscillators and the coupling forces between them. Here, we develop a single-cell assay that studies rhythmic behavior in the motility of individual E.coli cells that can be mutually synchronized. Circular microcavities are used to isolate E.coli cells that swim along the cavity wall, resulting in self-sustained oscillations. Upon connecting these cavities by microchannels the bacterial motions can be coupled, yielding nonlinear dynamic synchronization patterns with phase slips. We demonstrate that the coordinated movement observed in coupled E. coli oscillators follows mathematical rules of synchronization which we use to quantify the coupling strength. These findings advance our understanding of motility in confinement, and lay the foundation for engineering desired dynamics in microbial active matter.

The adoption of digital twins (DTs) in precision medicine is increasingly viable, propelled by extensive data collection and advancements in artificial intelligence (AI), alongside traditional biomedical methodologies. However, the reliance on black-box predictive models, which utilize large datasets, presents limitations that could impede the broader application of DTs in clinical settings. We argue that hypothesis-driven generative models, particularly multiscale modeling, are essential for boosting the clinical accuracy and relevance of DTs, thereby making a significant impact on healthcare innovation. This paper explores the transformative potential of DTs in healthcare, emphasizing their capability to simulate complex, interdependent biological processes across multiple scales. By integrating generative models with extensive datasets, we propose a scenario-based modeling approach that enables the exploration of diverse therapeutic strategies, thus supporting dynamic clinical decision-making. This method not only leverages advancements in data science and big data for improving disease treatment and prevention but also incorporates insights from complex systems and network science, quantitative biology, and digital medicine, promising substantial advancements in patient care.

Proteins fold to a specific functional conformation with a densely packed hydrophobic core that controls their stability. We develop a geometric, yet all-atom model for proteins that explains the universal core packing fraction of $\phi_c=0.55$ found in experimental measurements. We show that as the hydrophobic interactions increase relative to the temperature, a novel jamming transition occurs when the core packing fraction exceeds $\phi_c$. The model also recapitulates the global structure of proteins since it can accurately refold to native-like structures from partially unfolded states., Comment: 5 pages, 4 figures

Taking inspiration from the crawling motion of biological cells on a substrate, we consider a physical model of self-propulsion where the spatio-temporal driving can involve both, a mechanical actuation by active force couples, and a chemical actuation through controlled mass turnover. We show that the competition and cooperation between these two modalities of active driving can drastically broaden the performance repertoire of the crawler. When the material turnover is slow and the mechanical driving dominates, we find that the highest velocity at a given energetic cost is reached when actuation takes the form of an active force configuration propagating as a traveling wave. As the rate of material turnover increases, and the chemical driving starts to dominate the mechanical one, such a peristalsis-type control progressively loses its efficacy, yielding to a standing wave type driving which involves an interplay between the mechanical and chemical actuation. Our analysis suggests a new paradigm for the optimal design of crawling biomimetic robots where the conventional purely mechanical driving through distributed force actuators is complemented by a distributed chemical control of the material remodeling inside the force-transmitting machinery.

Intracellular protein patterns regulate many vital cellular functions, such as the processing of spatiotemporal information or the control of shape deformations. To do so, pattern-forming systems can be sensitive to the cell geometry by means of coupling the protein dynamics on the cell membrane to dynamics in the cytosol. Recent studies demonstrated that modeling the cytosolic dynamics in terms of an averaged protein pool disregards possibly crucial aspects of the pattern formation, most importantly concentration gradients normal to the membrane. At the same time, the coupling of two domains (surface and volume) with different dimensions renders many standard tools for the numerical analysis of self-organizing systems inefficient. Here, we present a generic framework for projecting the cytosolic dynamics onto the lower-dimensional surface that respects the influence of cytosolic concentration gradients in static and evolving geometries. This method uses a priori physical information about the system to approximate the cytosolic dynamics by a small number of dominant characteristic concentration profiles (basis), akin to basis transformations of finite element methods. As a proof of concept, we apply our framework to a toy model for volume-dependent interrupted coarsening, evaluate the accuracy of the results for various basis choices, and discuss the optimal basis choice for biologically relevant systems. Our analysis presents an efficient yet accurate method for analysing pattern formation with surface-volume coupling in evolving geometries., Comment: 16 pages, 11 figures

The formation of regularly structured silica valves in diatoms is a captivating process in biomineralization. Specific organic molecules, including long-chain polyamines, silaffins, and silacidins, have been found to be essential in this process. The molecular structure of synthesized polyamines greatly affects the quantity, size, and shape of silica precipitates. Experimental findings indicate that silica precipitation occurs at specific concentrations of phosphate ions, with higher concentrations resulting in larger aggregates of organic molecules that serve as templates for silica formation. Our study focuses on the hypothesis that pattern formation in diatom valve structures is driven by the phase separation of species-specific organic molecules, with the evolving organic structures acting as templates for silica deposition. We specifically investigate the role of phosphate ions in the self-assembly of long-chain polyamines and analyze how their reaction with organic molecules impacts the morphology of the organic template. By varying the model parameters, including degree of dissociation and initial concentrations of reacting components, we show that the resulting geometric features of the patterns are highly dependent on these factors. Furthermore, we explore the scenario where an initial array of organic droplets serves as a static template for silica deposition, considering the effects of "prepatterning" by the silica costae in the base layer. We obtain numerical solutions that generate a diverse range of two-dimensional patterns closely resembling the valve structures observed experimentally.

Transcranial focused ultrasound can deliver energy to the brain in a minimally invasive manner for a variety of clinical applications. However, acoustic inhomogeneities within the skull cause significant wave interactions, leading to difficulties in predicting the energy delivered to the target. We present a comprehensive examination of intracranial acoustic fields generated by focused ultrasound transducers and assess the characteristics of cranial bone that affect acoustic transmission. Acoustic field maps were generated at 88 regions of interest across 10 historical and 2 Thiel-embalmed human skull specimens with sonication at frequencies of 220 kHz, 650 kHz, and 1000 kHz. The average peak pressure attenuation was $3.1 \pm 1.4$ dB, $9.0 \pm 1.7$ dB, and $14.6 \pm 4.1$ dB, respectively. The average power attenuation was $5.0 \pm 2.4$ dB, $14.9 \pm 3.2$ dB, and $24.1 \pm 6.3$ dB, respectively. The effect of skull thickness, skull density ratio, and skull curvature on intracranial peak pressure and power was investigated and linear fits produced. Our results demonstrate that Thiel-embalmed samples fall within the confidence intervals of fits for historical samples. The effects of angulation and spacing between the transducer and the skull were also investigated. Results indicate that wave superposition resulting from skull and transducer spacing could lead to a 30-40% uncertainty in peak recorded intracranial pressure., Comment: 31 pages, 8 figures

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, F\"{o}rster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs $N_{r}$ that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, $N_{r}/N \lesssim 0.08$, where $N$ is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo.

The mechanisms leading cells to acquire a fitness advantage and establish themselves in a population are paramount to understanding the development and growth of cancer. Although there are many works that study separately either the evolutionary dynamics or the mechanics of cancer, little has been done to couple evolutionary dynamics to mechanics. To address this question, we study a confluent model of tissue using a Self-Propelled Voronoi (SPV) model with stochastic growth rates that depend on the mechanical variables of the system. The SPV model is an out-of-equilibrium model of tissue derived from an energy functional that has a jamming/unjamming transition between solid-like and liquid-like states. By considering several scenarios of mutants invading a resident population in both phases, we determine the range of parameters that confer a fitness advantage and show that the preferred area and perimeter are the most relevant ones. We find that the liquid-like state is more resistant to invasion and show that the outcome of the competition can be determined from the simulation of a non-growing mixture. Moreover, a mean-field approximation can accurately predict the fate of a mutation affecting mechanical properties of a cell. Our results can be used to infer evolutionary dynamics from tissue images, understand cancer-suppressing effects of tissue mechanics, and even search for mechanics-based therapies., Comment: 20 pages, 5 figures

Smart signal processing approaches using Artificial Intelligence are gaining momentum in NMR applications. In this study, we demonstrate that AI offers new opportunities beyond tasks addressed by traditional techniques. We developed and trained several artificial neural networks in our new toolbox Magnetic Resonance with Artificial intelligence (MR-Ai) to solve three "impossible" problems: quadrature detection using only Echo (or Anti-Echo) modulation from the traditional Echo/Anti-Echo scheme; accessing uncertainty of signal intensity at each point in a spectrum processed by any given method; and defining a reference-free score for quantitative access of NMR spectrum quality. Our findings highlight the potential of AI techniques to revolutionize NMR processing and analysis.

We discuss a generic description of the dynamics of cell aggregates. We describe cells as polar rotating objects which mechanically interact with each other and with the surrounding medium. We use the framework of non-equilibrium thermodynamics to derive generic constitutive equations for the interaction forces, torques and polarity dynamics. We apply our framework to discuss spontaneous motion of cell doublets. We find a rich phase diagram of possible collective motion, including steady rotation arising from flow-polarity coupling or coupling of polarity with cell position., Comment: 6 pages, 4 figures

Hemoglobin (Hb) is a multifaceted protein, classified as a metalloprotein, chromoprotein, and globulin. It incorporates iron, which plays a crucial role in transporting oxygen within red blood cells. Hb functions by carrying oxygen from the respiratory organs to diverse tissues in the body, where it releases oxygen to fuel aerobic respiration, thus supporting the organism's metabolic processes. Deviations in Hb concentration in the blood have been linked to various medical conditions, including anemia and other blood disorders. Here, we use optical detected magnetic relaxometry of paramagnetic iron spins in Hb drop-casted onto nanostructured diamond doped with shallow (~ 5.5 nm) high density nitrogen vacancy (NV) spin qubits. We modify the Hb concentration in the range of 6 x 10^6 to 1.8 x 10^7 adsorbed Fe+3 spins per um^2 and observe an increase of the NV relaxation rate G1 (= 1/ T1, T1 is NV spin lattice relaxation time) up to 2 x 10^3 s^-1. NV magnetic relaxometry of Hb in phosphate-buffered saline solution show a similar effect with an increase of G1 to 6.7 x 10^3 s^-1 upon increasing the Hb concentration to 100 uM. The increase of NV G1 is explained by the increased spin noise coming from the Fe+3 spins present in Hb proteins. This study presents an additional usage of NV quantum sensors to detect paramagnetic centers in biomolecules., Comment: arXiv admin note: text overlap with arXiv:2310.08605

Contractile biopolymer networks, such as the actomyosin meshwork of animal cells, are ubiquitous in living organisms. The active gel theory, which provides the thermodynamic framework for these materials, has been mostly used in conjunction with the assumption that the microstructure of the biopolymer network is based on rigid rods. However, experimentally, the actin network exhibits entropic elasticity. Here we combine an entropic elasticity kinetic theory, in the spirit of the Green and Tobolsky model of transiently crosslinked networks, with an active flux modelling biological activity. We determine this active flux using Onsager reciprocal relations and interpret the corresponding microscopic dynamics. We obtain a closed model of the macroscopic mechanical behaviour. We show how this model can be written using the framework of multiplicative strain gradient decomposition, which is convenient for the resolution of such problems.

We introduce a lattice model to probe the effect of active self-disassembly on equilibrium self-assembly. Surprisingly, we find conditions under which active self-disassembly enhances the yield of a target structure above that achieved by self-assembly alone when the latter is already favoured thermodynamically. We discuss biological implications of our findings., Comment: 5 pages, 4 figures

Biological tissues are complex structures composed of many elements which make light-based tissue diagnostics challenging. Over the past decades, Monte Carlo technique has been used as a fundamental and versatile approach toward modeling photon-tissue interactions. This report first describes a MC simulation of steady-state light transport in an absorbing and diffusing multi-layered structure. Further, a parallel processing solution is implemented to reduce execution time and memory requirements. Then, the nonparametric phase function, which is a discretized version of the phase function, has been discussed where the integration of the phase function is a numerical process, instead of an analytical operation. Finally, to simulate more realistic structures of biological systems, simulations are modified to incorporate objects of various shapes (sphere, ellipsoid, or cylinder) with a refractive-index mismatched boundary. The output files mainly contain 2D reflection matrix, 2D transmission matrix, and 3D absorption matrix., Comment: arXiv admin note: text overlap with arXiv:1711.03244 by other authors

Physics is living an era of unprecedented cross-fertilization among the different areas of science. In this perspective review, we discuss the manifold impact that ultracold-atom quantum technologies can have in fundamental and applied science through platforms for quantum simulation, computation, metrology and sensing. We illustrate how the engineering of table-top experiments with atom technologies is engendering applications to understand problems in condensed matter and fundamental physics, cosmology and astrophysics, foundational aspects of quantum mechanics, quantum chemistry and the emerging field of quantum biology. We take the perspective of two main approaches, i.e. creating quantum analogues and building quantum simulators, highlighting that independently of the ultimate goal of a universal quantum computer to be met, the remarkable transformative effects of these achievements remain unchanged. We convey three main messages. First, atomic quantum technologies have enabled a new way in which quantum technologies are used for fundamental science, even beyond the advancement of knowledge, which is characterised by truly cross-disciplinary research, extended interplay between theoretical and experimental thinking, and intersectoral approach. Second, quantum many-body physics is taking the center stage in frontier's science. Third, quantum science progress will have capillary impact on society. Thus, the adoption of a responsible research and innovation approach to quantum technologies is mandatory, to accompany citizens in building awareness and future scaffolding. Following on all these reflections, this perspective review is aimed at scientists active or interested in interdisciplinary research, providing the reader with an overview of the current status of these wide fields of research where ultracold-atomic platforms play a vital role in their description and simulation., Comment: 105 pages, 28 figures

Oxygen (O2)-sensing matrices are promising tools for the live monitoring of extracellular O2 consumption levels in long-term cell cultures. In this study, ratiometric O2-sensing membranes were prepared by electrospinning, an easy, low-cost, scalable, and robust method for fabricating nanofibers. Poly({\epsilon}-caprolactone) and poly(dimethyl)siloxane polymers were blended with tris(4,7-diphenyl-1,10-phenanthroline) ruthenium(II) dichloride, which was used as the O2-sensing probe, and rhodamine B isothiocyanate, which was used as the reference dye. The functionalized scaffolds were morphologically characterized by scanning electron microscopy, and their physicochemical profiles were obtained by Fourier transform infrared spectroscopy, thermogravimetric analysis, and water contact angle measurement. The sensing capabilities were investigated by confocal laser scanning microscopy, performing photobleaching, reversibility, and calibration curve studies toward different dissolved O2 (DO) concentrations. Electrospun sensing nanofibers showed a high response to changes in DO concentrations in the physiological-pathological range from 0.5 to 20% and good stability under ratiometric imaging. In addition, the sensing systems were highly biocompatible for cell growth promoting adhesiveness and growth of three cancer cell lines, namely metastatic melanoma cell line SK-MEL2, breast cancer cell line MCF-7, and pancreatic ductal adenocarcinoma cell line Panc-1, thus recreating a suitable biological environment in vitro. These O2-sensing biomaterials can potentially measure alterations in cell metabolism caused by changes in ambient O2 content during drug testing/validation and tissue regeneration processes., Comment: 15 pages, 4 figures

Methods of local topology are introduced to the field of protein physics. This is achieved by explaining how the folding and unfolding processes of a globular protein alter the local topology of the protein's C-alpha backbone through conformational bifurcations. The mathematical formulation builds on the concept of Arnol'd's perestroikas, by extending it to piecewise linear chains using the discrete Frenet frame formalism. In the low-temperature folded phase, the backbone geometry generalizes the concept of a Peano curve, with its modular building blocks modeled by soliton solutions of a discretized nonlinear Schroedinger equation. The onset of thermal unfolding begins when perestroikas change the flattening and branch points that determine the centers of solitons. When temperature increases, the perestroikas cascade, which leads to a progressive disintegration of the modular structures. The folding and unfolding processes are quantitatively characterized by a correlation function that describes the evolution of perestroikas under temperature changes. The approach provides a comprehensive framework for understanding the Physics of protein folding and unfolding transitions, contributing to the broader field of protein structure and dynamics., Comment: 18 pages, 11 figures

Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints -- either given by pairwise distances or the contactome -- play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. However, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent conflict by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics., Comment: 24 pages, 20 figures

A suspension of swimming microorganisms often generates a large-scale convective pattern known as bioconvection. In contrast to the thermal Rayleigh-Benard system, recent experimental studies report an emergence of steady localized convection patterns and bistability near the onset of instability in bioconvection systems. In this study, to understand the underlying mechanisms and identify the roles of particle self-propulsion in pattern formation, we theoretically and numerically investigate a model bioconvection system in a two-dimensional periodic boundary domain. In doing so, we extend a standard bioconvection model by introducing the equilibrium density profile as an independent parameter, for which the particle self-propulsion is treated as an independent dimensional parameter. Since the large-scale vertical structure dominates in this system, we are able to simplify the model by truncating the higher vertical modes. With this truncated model, we analytically derived the neutrally stable curve and found that the particle motility stabilizes the system. We then numerically analyzed the bifurcation diagram and found the bistable structure at the onset of instability. These findings, localization and bistability, are consistent with experimental observations. We further examined the global structure of the bistable dynamical system and found that the non-trivial unstable steady solution behaves as an edge state that separates the basins of attractors. These results highlight the importance of particle self-propulsion in bioconvection, and more generally our methodology based on the dynamical systems theory is useful in understanding complex flow patterns in nature., Comment: 10 pages, 9 figures

Chromosome capture techniques like Hi-C have expanded our understanding of mammalian genome 3D architecture and how it influences gene activity. To analyze Hi-C data sets, researchers increasingly treat them as DNA-contact networks and use standard community detection techniques to identify mesoscale 3D communities. However, there are considerable challenges in finding significant communities because the Hi-C networks have cross-scale interactions and are almost fully connected. This paper presents a pipeline to distil 3D communities that remain intact under experimental noise. To this end, we bootstrap an ensemble of Hi-C datasets representing noisy data and extract 3D communities that we compare with the unperturbed dataset. Notably, we extract the communities by maximizing local modularity (using the Generalized Louvain method), which considers the multifractal spectrum recently discovered in Hi-C maps. Our pipeline finds that stable communities (under noise) typically have above-average internal contact frequencies and tend to be enriched in active chromatin marks. We also find they fold into more nested cross-scale hierarchies than less stable ones. Apart from presenting how to systematically extract robust communities in Hi-C data, our paper offers new ways to generate null models that take advantage of the network's multifractal properties. We anticipate this has a broad applicability to several network applications.

Entrainment experiments on the vertebrate segmentation clock have revealed that embryonic oscillators actively change their internal frequency to adapt to the driving signal. This is neither consistent with a one-dimensional clock model nor with a limit-cycle model, but rather suggests a new "unclocklike" behavior. In this work, we propose simple biologically realistic descriptions of such internal frequency adaptation, where a phase oscillator activates a memory variable controlling the oscillator's frequency. We study two opposite limits for the control of the memory variable, one with a smooth phase-averaging memory field, and the other with a pulsatile, phase-dependent activation. Both models recapitulate intriguing properties of the entrained segmentation clock, such as very broad Arnold tongues and an entrainment phase plateauing with detuning. We compute analytically multiple properties of such systems, such as the entrainment phases and cycle shapes. We further describe new phenomena, including hysteresis in entrainment, bistability in the frequency of the entrained oscillator, and probabilistic entrainment. Our work shows oscillators with frequency memory can exhibit new classes of unclocklike properties, that can be tested experimentally.

While the linear Pearson correlation coefficient represents a well-established normalized measure to quantify the interrelation of two stochastic variables $X$ and $Y$, it fails for multidimensional variables such as Cartesian coordinates. Avoiding any assumption about the underlying data, the mutual information $I(X, Y)$ does account for multidimensional correlations. However, unlike the normalized Pearson correlation, it has no upper bound ($I \in [0, \infty)$), i.e., it is not clear if say, $I = 0.4$ corresponds to a low or a high correlation. Moreover, the mutual information (MI) involves the estimation of high-dimensional probability densities (e.g., six-dimensional for Cartesian coordinates), which requires a k-nearest neighbor algorithm, such as the estimator by Kraskov et al. [Phys. Rev. E 69, 066138 (2004)]. As existing methods to normalize the MI cannot be used in connection with this estimator, a new approach is presented, which uses an entropy estimation method that is invariant under variable transformations. The algorithm is numerically efficient and does not require more effort than the calculation of the (un-normalized) MI. After validating the method by applying it to various toy models, the normalized MI between the $C_{\alpha}$ -coordinates of T4 lysozyme is considered and compared to a correlation analysis of inter-residue contacts.

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that natural selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.

How life started on Earth is a long-time unsolved mystery. There are various hypotheses ranging from outer space to seabed. Here, we applied extensive ab initio molecular dynamics (AIMD) simulations to study chemical reactions of NH3, H2O, H2, and CO at pressures (P) and temperatures (T) approximating the conditions of Earth's upper mantle (i.e. 10 - 13 GPa, 1000 -1400 K). Contrary to the previous assumption that larger organic molecules might readily dissociate in aqueous solutions at extreme P-T conditions, we found that many organic compounds formed and persisted in C-H-O-N fluids under these extreme conditions, including glycine, ribose, and uracil-like molecules. Particularly, our free energy calculations showed that the C-N bond is thermodynamically stable at 10 GPa and 1400 K. Moreover, our findings support the "RNA world" hypothesis, as we observed the exclusive formation of the 5-membered-ring form of ribose. By exploring the depths of Earth's interior, we have uncovered a previously unexplored pathway through which life may have originated. These findings have contributed to our evolving understanding of the fundamental conditions necessary for life to arise on our planet., Comment: 18 pages, 6 figures, 1 table

Neuronal dendrites form densely branched tree architectures through which mitochondria must be distributed to supply the cell's energetic needs. Dendritic mitochondria circulate through the tree, undergoing fusion and fission to form clusters of varying sizes. We present a mathematical model for the distribution of such actively-driven particles in a branched geometry. Our model demonstrates that `balanced' trees (wherein cross-sectional area is conserved across junctions and thicker branches support more bushy subtrees) enable symmetric yet distally enriched particle distributions and promote dispersion into smaller clusters. These results highlight the importance of tree architecture and radius-dependent fusion in governing the distribution of neuronal mitochondria.

Critical experimental design issues connecting energy transduction and inheritable information within a protocell are explored and elucidated. The protocell design utilizes a photo-driven energy transducer (a ruthenium complex) to turn resource molecules into building blocks, in a manner that is modulated by a combinatorial DNA-based co-factor. This co-factor molecule serves as part of an electron relay for the energy transduction mechanism, where the charge-transport rates depend on the sequence that contains an oxo-guanine. The co-factor also acts as a store of inheritable information due to its ability to replicate non-enzymatically through template-directed ligation. Together, the energy transducer and the co-factor act as a metabolic catalyst that produces co-factor DNA building blocks as well as fatty acids (from picolinium ester and modified DNA oligomers), where the fatty acids self-assemble into vesicles on which exterior surface both the co-factor (DNA) and the energy transducer are anchored with hydrophobic tails. Here we use simulations to study how the co-factor sequence determines its fitness as reflected by charge transfer and replication rates. To estimate the impact on the protocell, we compare these rates with previously measured metabolic rates from a similar system where the charge transfer is directly between the ruthenium complex and the oxo-guanine (without DNA replication and charge transport). Replication and charge transport turn out to have different and often opposing sequence requirements. Functional information of the co-factor molecules is used to probe the feasibility of randomly picking co-factor sequences from a limited population of co-factors molecules, where a good co-factor can enhance both metabolic biomass production and its own replication rate.

The large-conductance, calcium-activated potassium (BK) channel lacks the typical intracellular bundle-crossing gate present in most ion channels of the 6TM family. This observation, initially inferred from Ca$^{2+}$-free-pore accessibility experiments and recently corroborated by a CryoEM structure of the non-conductive state, raises a puzzling question: how can gating occur in absence of steric hindrance? To answer this question, we carried out molecular simulations and accurate free energy calculations to obtain a microscopic picture of the sequence of events that, starting from a Ca$^{2+}$-free state leads to ion conduction upon Ca$^{2+}$ binding. Our results highlight an unexpected role for annular lipids, which turn out to be an integral part of the gating machinery. Due to the presence of fenestrations, the "closed" Ca$^{2+}$-free pore can be occupied by the methyl groups from the lipid alkyl chains. This dynamic occupancy triggers and stabilizes the nucleation of a vapor bubble into the inner pore cavity, thus hindering ion conduction. By contrast, Ca$^{2+}$ binding results into a displacement of these lipids outside the inner cavity, lowering the hydrophobicity of this region and thus allowing for pore hydration and conduction. This lipid-mediated hydrophobic gating rationalizes several seemingly problematic experimental observations, including the state-dependent pore accessibility of blockers.

$^{18}$F-Fluoromisonidazole ($^{18}$F-FMISO) is a highly promising positron emission tomography radiopharmaceutical for identifying hypoxic regions in solid tumors. This research employs spatiotemporal multi-scale mathematical modeling to explore how different levels of angiogenesis influence the transport of radiopharmaceuticals within tumors. In this study, two tumor geometries with heterogeneous and uniform distributions of capillary networks were employed to incorporate varying degrees of microvascular density. The synthetic image of the heterogeneous and vascularized tumor was generated by simulating the angiogenesis process. The proposed multi-scale spatiotemporal model accounts for intricate physiological and biochemical factors within the tumor microenvironment, such as the transvascular transport of the radiopharmaceutical agent, its movement into the interstitial space by diffusion and convection mechanisms, and ultimately its uptake by tumor cells. Results showed that both quantitative and semi-quantitative metrics of $^{18}$F-FMISO uptake differ spatially and temporally at different stages during tumor growth. The presence of a high microvascular density in uniformly vascularized tumor increases cellular uptake, as it allows for more efficient release and rapid distribution of radiopharmaceutical molecules. This results in enhanced uptake compared to the heterogeneous vascularized tumor. In both heterogeneous and uniform distribution of microvessels in tumors, the diffusion transport mechanism has a more pronounced than convection. The findings of this study shed light on the transport phenomena behind $^{18}$F-FMISO radiopharmaceutical distribution and its delivery in the tumor microenvironment, aiding oncologists in their routine decision-making processes.

The emergence of label-free microscopy techniques has significantly improved our ability to precisely characterize biochemical targets, enabling non-invasive visualization of cellular organelles and tissue organization. Each label-free method has specific benefits, drawbacks, and varied varied sensitivity under measurement conditions across different types of specimens. To link all these disparate label-free optical interactions together and to compare detection sensitivity of these modalities, we investigate their sensitivity within the framework of statistical estimation theory. This paper introduces a comprehensive unified framework for evaluating the bounds for signal detection with label-free microscopy methods, including second harmonic generation (SHG), third harmonic generation (THG), coherent anti-Stokes Raman scattering (CARS), coherent Stokes Raman scattering (CSRS), stimulated Raman loss (SRL), stimulated Raman gain (SRG), stimulated emission (SE), impulsive stimulated Raman scattering (ISRS), transient absorption (TA), and photothermal effect (PTE). A general model for signal generation induced by optical scattering is developed. Based on this model, the information obtained is quantitatively analyzed using Fisher information, and the fundamental constraints on estimation precision are evaluated through the Cram\'er-Rao Lower Bound (CRLB)., Comment: 17 pages, 4 figures, 1 table

Chirality-induced spin selectivity is a spin-splitting phenomenon from a helical structure with a considerably effective spin-orbit coupling. This unexpectedly large spin-splitting phenomenon has been experimentally observed in chiral organic molecules, which typically show a weak spin-orbit coupling. To understand this, we use the renormalized mean-field theory and Landauer-B\"{u}ttiker formulas to study the transport properties of single-stranded DNA in the presence of strong electron correlation. It shows a significant spin polarization of 46.5% near the Coulomb repulsion limit, which explains the extremely high spin polarization observed in experiments. Compared to systems without electron correlation, the averaged spin polarization in this case is 2 to 4 times greater across various system sizes. Furthermore, the parameter dependence of the spin polarization and the underlying Metal-Insulator transition are studied.

We develop an action formalism to calculate probabilities of rare events in cluster-cluster aggregation for arbitrary collision kernels and establish a pathwise large deviation principle with total mass being the rate. As an application, the rate function for the number of surviving particles as well as the optimal evolution trajectory are calculated exactly for the constant, sum and product kernels. For the product kernel, we argue that the second derivative of the rate function has a discontinuity. The theoretical results agree with simulations tailored to the calculation of rare events.

The integration of neural representations in the two hemispheres is an important problem in neuroscience. Recent experiments revealed that odor responses in cortical neurons driven by separate stimulation of the two nostrils are highly correlated. This bilateral alignment points to structured inter-hemispheric connections, but detailed mechanism remains unclear. Here, we hypothesized that continuous exposure to environmental odors shapes these projections and modeled it as online learning with local Hebbian rule. We found that Hebbian learning with sparse connections achieves bilateral alignment, exhibiting a linear trade-off between speed and accuracy. We identified an inverse scaling relationship between the number of cortical neurons and the inter-hemispheric projection density required for desired alignment accuracy, i.e., more cortical neurons allow sparser inter-hemispheric projections. We next compared the alignment performance of local Hebbian rule and the global stochastic-gradient-descent (SGD) learning for artificial neural networks. We found that although SGD leads to the same alignment accuracy with modestly sparser connectivity, the same inverse scaling relation holds. We showed that their similar performance originates from the fact that the update vectors of the two learning rules align significantly throughout the learning process. This insight may inspire efficient sparse local learning algorithms for more complex problems.

Recent experimental investigations into Hydra regeneration revealed a remarkable phenomenon: the morphological transformation of a tissue fragment from the incipient spherical configuration to a tube-like structure - the hallmark of a mature Hydra - has the dynamical characteristics of a first-order phase-transition, with calcium field fluctuations within the tissue playing an essential role. This morphological transition was shown to be generated by activation over an energy barrier within an effective potential that underlies morphogenesis. Inspired by this intriguing insight, we propose a novel mechanism where stochastic fluctuations drive the emergence of morphological patterns. Thus, the inherent fluctuations determine the nature of the dynamics and are not incidental noise in the background of the otherwise deterministic dynamics. Instead, they play an important role as a driving force that defines the attributes of the pattern formation dynamics and the nature of the transition itself. Here, we present a simple model that captures the essence of this novel mechanism for morphological pattern formation. Specifically, we consider a one-dimensional tissue arranged as a closed contour embedded in a two-dimensional space, where the local curvature of the contour is coupled to a non-negative scalar field. An effective temperature parameter regulates the strength of the fluctuations in the system. The tissue exhibits fluctuations near a circular shape at sufficiently low coupling strengths, but as the coupling strength exceeds some critical value, the circular state becomes unstable. The nature of the transition, namely whether it is a first or a second-order-like transition, depends on the temperature and the effective cutoff on the wavelength of the spatial variations in the system. It is also found that entropic barriers separate the various metastable states of the system., Comment: 22 pages, 11 figures

The task of olfactory search is ubiquitous in nature and in technology, from animals in the quest of food or of a mating partner, to robots searching for the source of hazardous fumes in a chemical plant. Here, we focus on the algorithmic approach to this task: we systematically review the different olfactory search strategies. Special emphasis is given to the formal description as a Partially Observable Markov Decision Processes, which allows the computation of optimal actions and helps clarifying the relationships between several effective heuristic search strategies., Comment: 22 pages, 1 figure, review

Homeostasis is concerned with regulatory mechanisms, present in biological systems, where some specific variable is kept close to a set value as some external disturbance affects the system. Mathematically, the notion of homeostasis can be formalized in terms of an input-output function that maps the parameter representing the external disturbance to the output variable that must be kept within a fairly narrow range. This observation inspired the introduction of the notion of infinitesimal homeostasis, namely, the derivative of the input-output function is zero at an isolated point. This point of view allows for the application of methods from singularity theory to characterize infinitesimal homeostasis points (i.e. critical points of the input-output function). In this paper we review the infinitesimal approach to the study of homeostasis in input-output networks. An input-output network is a network with two distinguished nodes `input' and `output', and the dynamics of the network determines the corresponding input-output function of the system. This class of dynamical systems provides an appropriate framework to study homeostasis and several important biological systems can be formulated in this context. Moreover, this approach, coupled to graph-theoretic ideas from combinatorial matrix theory, provides a systematic way for classifying different types of homeostasis (homeostatic mechanisms) in input-output networks, in terms of the network topology. In turn, this leads to new mathematical concepts, such as, homeostasis subnetworks, homeostasis patterns, homeostasis mode interaction. We illustrate the usefulness of this theory with several biological examples: biochemical networks, chemical reaction networks (CRN), gene regulatory networks (GRN), Intracellular metal ion regulation and so on., Comment: 45 pages, 26 figures, submitted to the MBS special issue "Dynamical Systems in Life Sciences"

Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered proteins (IDPs). However much of protein function is modulated beyond the translated sequence through thFiguree introduction of post-translational modifications (PTMs). In this work we have provided a curated set of side chain rotamers for the most common PTMs derived from the RCSB PDB database, including phosphorylated, methylated, and acetylated sidechains. Our rotamer libraries improve upon existing methods such as SIDEpro and Rosetta in predicting the experimental structures for PTMs in folded proteins. In addition, we showcase our PTM libraries in full use by generating ensembles with the Monte Carlo Side Chain Entropy (MCSCE) for folded proteins, and combining MCSCE with the Local Disordered Region Sampling algorithms within IDPConformerGenerator for proteins with intrinsically disordered regions.

We study a five-species cyclic system wherein individuals of one species strategically adapt their movements to enhance their performance in the spatial rock-paper-scissors game. Environmental cues enable the awareness of the presence of organisms targeted for elimination in the cyclic game. If the local density of target organisms is sufficiently high, individuals move towards concentrated areas for direct attack; otherwise, they employ an ambush tactic, maximising the chances of success by targeting regions likely to be dominated by opponents. Running stochastic simulations, we discover that the ambush strategy enhances the likelihood of individual success compared to direct attacks alone, leading to uneven spatial patterns characterised by spiral waves. We compute the autocorrelation function and measure how the ambush tactic unbalances the organisms' spatial organisation by calculating the characteristic length scale of typical spatial domains of each species. We demonstrate that the threshold for local species density influences the ambush strategy's effectiveness, while the neighbourhood perception range significantly impacts decision-making accuracy. The outcomes show that long-range perception improves performance by over 60\%, although there is potential interference in decision-making under high attack triggers. Understanding how organisms' adaptation to their environment enhances their performance may be helpful not only for ecologists but also for data scientists aiming to improve artificial intelligence systems., Comment: 8 pages, 5 figures

The multi-grid reaction-diffusion master equation (mgRDME) provides a generalization of stochastic compartment-based reaction-diffusion modelling described by the standard reaction-diffusion master equation (RDME). By enabling different resolutions on lattices for biochemical species with different diffusion constants, the mgRDME approach improves both accuracy and efficiency of compartment-based reaction-diffusion simulations. The mgRDME framework is examined through its application to morphogen gradient formation in stochastic reaction-diffusion scenarios, using both an analytically tractable first-order reaction network and a model with a second-order reaction. The results obtained by the mgRDME modelling are compared with the standard RDME model and with the (more detailed) particle-based Brownian dynamics simulations. The dependence of error and numerical cost on the compartment sizes is defined and investigated through a multi-objective optimization problem.

Local stresses in a tissue, a collective property, regulate cell division and apoptosis. In turn, cell growth and division induce active stresses in the tissue. As a consequence, there is a feedback between cell growth and local stresses. However, how the cell dynamics depend on local stress-dependent cell division and the feedback strength is not fully understood. Here, we probe the consequences of stress-mediated growth and cell division on cell dynamics using agent-based simulations of a two-dimensional growing tissue. We discover a rich dynamical behavior of individual cells, ranging from jamming (mean square displacement, $\Delta (t) \sim t^{\alpha}$ with $\alpha$ less than unity), to hyperdiffusion ($\alpha > 2$) depending on cell division rate and the strength of the mechanical feedback. Strikingly, $\Delta (t)$ is determined by the tissue growth law, which quantifies cell proliferation (number of cells $N(t)$ as a function of time). The growth law ($N(t) \sim t^{\lambda}$ at long times) is regulated by the critical pressure that controls the strength of the mechanical feedback and the ratio between cell division-apoptosis rates. We show that $\lambda \sim \alpha$, which implies that higher growth rate leads to a greater degree of cell migration. The variations in cell motility are linked to the emergence of highly persistent forces extending over several cell cycle times. Our predictions are testable using cell-tracking imaging techniques., Comment: 5 figures. arXiv admin note: text overlap with arXiv:2202.04806

Mixtures with many components can segregate into coexisting phases, e.g., in biological cells and synthetic materials such as metallic glass. The interactions between components dictate what phases form in equilibrium, but quantifying this relationship has proven difficult. We derive scaling relations for the number of coexisting phases in multicomponent liquids with random interactions and compositions, which we verify numerically. Our results indicate that interactions only need to increase logarithmically with the number of components for the liquid to segregate into many phases. In contrast, a stability analysis of the homogeneous state predicts a power-law scaling. This discrepancy implies an enormous parameter regime where the number of coexisting phases exceeds the number of unstable modes, generalizing the nucleation and growth regime of binary mixtures to many components., Comment: 15 pages, 10 figures, including appendix

Dissipative environments are ubiquitous in nature, from microscopic swimmers in low-Reynolds-number fluids to macroscopic animals in frictional media. In this study, motivated by various behaviours of {\it Caenorhabditis elegans} during swimming and crawling locomotion, we consider a mathematical model of a slender elastic locomotor with an internal rhythmic neural pattern generator. By analysing the dynamical systems of the model using a Poincar\'e section, we found that local neuromechanical adjustments to the environment can create robust undulatory locomotion. This progressive behaviour emerges as a global stable periodic orbit in a broad range of parameter regions. Further, by controlling the mechanosensation, we were able to design the dynamical systems to manoeuvre with progressive, reverse, and turning motions as well as apparently random, complex behaviours, as experimentally observed in {\it C. elegans}. The mechanisms found in this study, together with our methodologies with the dynamical systems viewpoint, are useful for deciphering complex animal adaptive behaviours and also designing adaptive robots for a wide range of dissipative environments., Comment: 13 pages, 9 figures

This paper presents an efficient finite element iterative method for solving a nonuniform size-modified Poisson-Nernst-Planck ion channel (SMPNPIC) model, along with a SMPNPIC program package that works for an ion channel protein with a three-dimensional crystallographic structure and an ionic solvent with multiple ionic species. In particular, the SMPNPIC model is constructed and then reformulated by novel mathematical techniques so that each iteration of the method only involves linear boundary value problems and nonlinear algebraic systems, circumventing the numerical difficulties caused by the strong nonlinearities, strong asymmetries, and strong differential equation coupling of the SMPNPIC model. To further improve the method's efficiency, an efficient modified Newton iterative method is adapted to the numerical solution of each related nonlinear algebraic system. Numerical results for a voltage-dependent anion channel (VDAC) and a mixture solution of four ionic species demonstrate the method's convergence, the package's high performance, and the importance of considering nonuniform ion size effects. They also partially validate the SMPNPIC model by the anion selectivity property of VDAC., Comment: 24 pages, 5 figures, 3 tables

Stretching an elastic material along one axis typically induces contraction along the transverse axes, a phenomenon known as the Poisson effect. From these strains, one can compute the specific volume, which generally either increases or, in the incompressible limit, remains constant as the material is stretched. However, in networks of semiflexible or stiff polymers, which are typically highly compressible yet stiffen significantly when stretched, one instead sees a significant reduction in specific volume under finite strains. This volume reduction is accompanied by increasing alignment of filaments along the strain axis and a nonlinear elastic response, with stiffening of the apparent Young's modulus. For semiflexible networks, in which entropic bending elasticity governs the linear elastic regime, the nonlinear Poisson effect is caused by the nonlinear force-extension relationship of the constituent filaments, which produces a highly asymmetric response of the constituent polymers to stretching and compression. The details of this relationship depend on the geometric and elastic properties of the underlying filaments, which can vary greatly in experimental systems. Here, we provide a comprehensive characterization of the nonlinear Poisson effect in an affine network model and explore the influence of filament properties on essential features of the macroscopic response, including strain-driven alignment and volume reduction.

Collectively moving cellular systems often contain a proportion of dead cells or non-motile genotypes. When mixed, nematically aligning motile and non-motile agents are known to segregate spontaneously. However, the role that topological defects and active stresses play in shaping the distribution of the two phases remains unresolved. In this study, we investigate the behaviour of a two-dimensional binary mixture of active and passive nematic fluids to understand how topological defects are transported between the two phases and, ultimately, how this leads to the segregation of topological charges. When the activity of the motile phase is large, and the tension at the interface of motile and non-motile phases is weak, we find that the active phase tends to accumulate $+1/2$ defects and expel $-1/2$ defects so that the motile phase develops a net positive charge. Conversely, when the activity of the motile phase is comparatively small and interfacial tension is strong, the opposite occurs so that the active phase develops a net negative charge. We then use these simulations to develop a physical intuition of the underlying processes that drive the charge segregation. Lastly, we quantify the sensitivity of this process on the other model parameters, by exploring the effect that anchoring strength, orientational elasticity, friction, and volume fraction of the motile phase have on topological charge segregation. As $+1/2$ and $-1/2$ defects have very different effects on interface morphology and fluid transport, this study offers new insights into the spontaneous pattern formation that occurs when motile and non-motile cells interact.

The formation of protein precursors, due to the condensation of atomic carbon under the low-temperature conditions of the molecular phases of the interstellar medium, opens alternative pathways for the origin of life. We perform peptide synthesis under conditions prevailing in space and provide a comprehensive analytic characterization of its products. The application of 13C allowed us to confirm the suggested pathway of peptide formation that proceeds due to the polymerization of aminoketene molecules that are formed in the C + CO + NH3 reaction. Here, we address the question of how the efficiency of peptide production is modified by the presence of water molecules. We demonstrate that although water slightly reduces the efficiency of polymerization of aminoketene, it does not prevent the formation of peptides.

Photosynthetic algae play a significant role in oceanic carbon capture. Their performance, however, is constantly challenged by fluctuations in environmental light conditions. Here, we show that the non-motile single-celled marine dinoflagellate Pyrocystis lunula can internally contract its chloroplast network in response to light. By exposing the cell to various physiological light conditions and applying temporal illumination sequences, we find that network morphodynamics follows simple rules, as established in a mathematical model. Our analysis of the chloroplast structure reveals that its unusual reticulated morphology constitutes properties similar to auxetic metamaterials, facilitating drastic deformations for light-avoidance, while confined by the cell wall. Our study shows how the topologically complex network of chloroplasts is crucial in supporting the dinoflagellate's adaptation to varying light conditions, thereby facilitating essential life-sustaining processes.