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We review the literature on swimming in complex fluids. A classification is proposed by comparing the length and time scales of a swimmer with those of nearby obstacles, interpreted broadly, extending from rigid or soft confining boundaries to molecules which confer the bulk fluid with complex stresses. A third dimension in the classification is the concentration of swimmers, which incorporates fluids whose complexity arises purely by the collective motion of swimming organisms. For each of the eight system classes which we identify we provide a background and describe modern research findings. While some classes have seen a great deal of attention for decades, others remain uncharted waters still open and awaiting exploration., Comment: 31 pages, 13 figures, to appear in Annual Review of Condensed Matter Physics

The blastocoel is a fluid-filled cavity characteristic of animal embryos at the blastula stage. Its emergence is commonly described as the result of cleavage patterning, but this historical view conceals a large diversity of mechanisms and overlooks many unsolved questions from a biophysics perspective. In this review, we describe generic mechanisms for blastocoel morphogenesis, rooted in biological literature and simple physical principles. We propose novel directions of study and emphasize the importance to study blastocoel morphogenesis as an evolutionary and physical continuum., Comment: 26 pages, 6 figures, review

With the growth of artificial intelligence (AI), there has been an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes, but it has also started supporting advances in drug development, precision medicine and genome-phenome mapping. Here we survey existing AI-based techniques and tools, and open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from the computational perspective and review emerging research frontiers and innovative applications for deep learning-guided automation for cell dynamics research.

Angiogenesis is the process wherein endothelial cells (ECs) form sprouts that elongate from the pre-existing vasculature to create new vascular networks. In addition to its essential role in normal development, angiogenesis plays a vital role in pathologies such as cancer, diabetes and atherosclerosis. Mathematical and computational modelling has contributed to unravelling its complexity. Many existing theoretical models of angiogenic sprouting are based on the 'snail-trail' hypothesis. This framework assumes that leading ECs positioned at sprout tips migrate towards low-oxygen regions while other ECs in the sprout passively follow the leaders' trails and proliferate to maintain sprout integrity. However, experimental results indicate that, contrary to the snail-trail assumption, ECs exchange positions within developing vessels, and the elongation of sprouts is primarily driven by directed migration of ECs. The functional role of cell rearrangements remains unclear. This review of the theoretical modelling of angiogenesis is the first to focus on the phenomenon of cell mixing during early sprouting. We start by describing the biological processes that occur during early angiogenesis, such as phenotype specification, cell rearrangements and cell interactions with the microenvironment. Next, we provide an overview of various theoretical approaches that have been employed to model angiogenesis, with particular emphasis on recent in silico models that account for the phenomenon of cell mixing. Finally, we discuss when cell mixing should be incorporated into theoretical models and what essential modelling components such models should include in order to investigate its functional role., 26 pages, 9 figures, 1 table. Submitted for publication to WIREs Mechanisms of Disease

Cell size control and homeostasis is a major topic in cell biology yet to be fully understood. Several growth laws like the timer, adder, and sizer were proposed, and mathematical approaches that model cell growth and division were developed. This study focuses on utilizing stochastic map modeling for investigating asymmetric cell division. We establish a mapping between the description of cell growth and division and the Ornstein-Uhlenbeck process with dichotomous noise. We leverage this mapping to achieve analytical solutions and derive a closed-form expression for the stable cell size distribution under asymmetric division. To validate our findings, we conduct numerical simulations encompassing several cell growth scenarios. Our approach allows us to obtain a precise criterion for a bi-phasic behavior of the cell size. While for the case of the sizer scenario, a transition from the uni-modal phase to bi-modal is always possible, given sufficiently large asymmetry at the division, the affine-linear approximation of the adder scenario invariably yields uni-modal distribution.

The recent discovery of the striking sheet-like multicellular choanoflagellate species $Choanoeca~flexa$ that dynamically interconverts between two hemispherical forms of opposite orientation raises fundamental questions in cell and evolutionary biology, as choanoflagellates are the closest living relatives of animals. It similarly motivates questions in fluid and solid mechanics concerning the differential swimming speeds in the two states and the mechanism of curvature inversion triggered by changes in the geometry of microvilli emanating from each cell. Here we develop fluid dynamical and mechanical models to address these observations and show that they capture the main features of the swimming, feeding, and inversion of $C.~flexa$ colonies., Comment: 5 pages, 3 figures + Supplementary Material (videos on request from REG)

Breast cancer is the leading type of cancer in women. About 10-15% of breast cancers are triple-negative breast cancer (TNBC), a subtype with the worst prognosis. Due to the lack of estrogen, progesterone and HER2 receptor expression, chemotherapies have been the standard of care for decades. Immunotherapy has emerged as promising for TNBC treatment. In 2020, the Food and Drug Administration (FDA) granted approval to pembrolizumab in combination with chemotherapy for patients with advanced triple-negative breast cancer. However, only a subgroup of advanced TNBC patients live longer whose tumors have a PD-L1 Combined Positive Score of at least 10 (CPS>=10). There is still an unmet medical need to provide alternative treatment for the rest of patients. Interestingly, a few of patients at UCSD Moores Cancer Center were found to have had excellent responses to pembrolizumab despite low CPS scores (termed Elite Responders). The hypothesis of this project is that there may be an alternative immune response mechanism and/or crosstalk happening between the innate and adaptive immune systems, especially in Natural Killer Cells and Macrophages, that contributed to this unexpected excellent response. Our procedure used ACDBio RNAscope Multiplex Fluorescence v2 method to spatially analyze innate immune cells (Natural Killer cells and macrophages) and adaptive immune cells (T-cells) in the Tumor Micro Environment. Our data demonstrated increased tumor infiltration of innate immune cells (macrophage and Natural Killer cells) in the Elite Responders. This conclusion indicated the joint effort of two immune systems (innate and adaptive) which eventually led to increased survival.

Uncontrolled growth of tumor cells in confined spaces leads to the accumulation of compressive stress within the tumor. Although the effects of tension within 3D extracellular matrices on tumor growth and invasion are well established, the role of compression in tumor mechanics and invasion is largely unexplored. In this study, we modified a Transwell assay such that it provides constant compressive loads to spheroids embedded within a collagen matrix. We used microscopic imaging to follow the single cell dynamics of the cells within the spheroids, as well as invasion into the 3D extracellular matrices (EMCs). Our experimental results showed that malignant breast tumor (MDA-MB-231) and non-tumorigenic epithelial (MCF10A) spheroids responded differently to a constant compression. Cells within the malignant spheroids became more motile within the spheroids and invaded more into the ECM under compression; whereas cells within non-tumorigenic MCF10A spheroids became less motile within the spheroids and did not display apparent detachment from the spheroids under compression. These findings suggest that compression may play differential roles in healthy and pathogenic epithelial tissues and highlights the importance of tumor mechanics and invasion., 10 pages, 5 figure and 3 supplementary figures

The constant interplay and information exchange between cells and their micro-environment are essential to their survival and ability to execute biological functions. To date, a few leading technologies such as traction force microscopy, have been broadly used in measuring cellular forces. However, the considerable limitations, regarding the sensitivity and ambiguities in data interpretation, are hindering our thorough understanding of mechanobiology. Herein, we propose an innovative approach, namely quantum-enhanced diamond molecular tension microscopy (QDMTM), to precisely quantify the integrin-based cell adhesive forces. Specifically, we construct a force sensing platform by conjugating the magnetic nanotags labeled, force-responsive polymer to the surface of diamond membrane containing nitrogen vacancy (NV) centers. Thus, the coupled mechanical information can be quantified through optical readout of spin relaxation of NV centers modulated by those magnetic nanotags. To validate QDMTM, we have carefully performed corresponding measurements both in control and real cell samples. Particularly, we have obtained the quantitative cellular adhesion force mapping by correlating the measurement with established theoretical model. We anticipate that our method can be routinely used in studying important issues like cell-cell or cell-material interactions and mechanotransduction., 51 pages, 20 figures

Cell size exhibits a huge variability across different kinds of cells. However, cells of isogenic populations have a well defined typical size, preserved despite the complex and noisy machinery of cellular processes. How this is achieved is a question that remains still largely unanswered. From a reductionist cell dynamics point of view, the strategies allowing size homeostasis are roughly grouped into three classes, i.e. timer, sizer or adder. These strategies are empirically distinguishable given the phenomenological measurable relationship between the cell size at birth and at division. Here, we show how it is possible to infer the growth regime and division strategy using flow cytometric data of properly marked cells and forward side scattering as a proxy for cell size. In particular, comparing the experimental data with the prediction of a minimal mathematical model, we found that (i) leukemia Jurkat T-cells in standard growth conditions follow a sizer-like strategy, with (ii) division times following an Erlang distribution given by the sum of at least three independent exponentially-distributed times. Moreover, our work shows that (iii) the dynamics of the size distribution is reproduced by a minimal model where both cell growth and division rates depends on powers of the cell size. Finally, we note that our experimental and theoretical apparatus can be easily extended to other cell types and environmental conditions, allowing for a comprehensive characterization of the growth and division model different cells can adopt., 15 pages, 5 figures

The discovery of general principles underlying the complexity and diversity of cellular and developmental systems is a central and long-standing aim of biology. Whilst new technologies collect data at an ever-accelerating rate, there is growing concern that conceptual progress is not keeping pace. We contend that this is due to a paucity of appropriate conceptual frameworks to serve as a basis for general theories of mesoscale biological phenomena. In exploring this issue, we have developed a foundation for one such framework, termed the Core and Periphery (C&P) hypothesis, which reveals hidden generality across the diverse and complex behaviors exhibited by cells and tissues. Here, we present the C&P concept, provide examples of its applicability across multiple scales, argue its consistency with evolution, and discuss key implications and open questions. We propose that the C&P hypothesis could unlock new avenues of conceptual progress in cell and developmental biology., 35 pages, 5 figures

During multi-cellular development, highly reproducible gene expression patterns determine cellular fates precisely in time and space. These processes are crucial during the earliest stages when the body plan and the future asymmetric body axes emerge at gastrulation. In some species, such as flies and worms, these early processes achieve near-single-cell spatial precision, even for macroscopic patterns. However, we know little about such accuracy in mammalian development, where quantitative approaches are limited. Using an in vitro model for mammalian development, i.e., gastruloids, we demonstrate that gene expression patterns are reproducible to within 20% in protein concentration variability, which translates to a positional error close to a single cell diameter at the tissue scale. In addition, 2-3 fold system size changes lead to scaled gene expression patterns again on the order of an individual cell diameter. Our results reveal developmental precision, reproducibility, and size scaling for mammalian systems. All three properties spontaneously arise in self-organizing cell aggregates and could thus be fundamental features of multicellularity.

Gene expression patterns are established by cross-regulating target genes that interpret morphogen gradients. However, as development progresses, morphogen activity is reduced, leaving the emergent pattern without stabilizing positional cues. The pattern then can be deteriorated by the intrinsically noisy biochemical processes acting at the cellular level. But remarkably, the established gene expression patterns remain spatially and temporally stable in many biological systems. Here we combine spatial-stochastic simulations with an enhanced sampling method and a recently developed stability theory to address how spatiotemporal integrity of a gene expression pattern is maintained in developing tissue lacking morphogen gradients. Using a minimal embryo model consisting of spatially coupled biochemical reactor volumes, we study a stripe pattern in which weak cross-repression between nearest neighbor domians alternates with strong repression between next-nearest neighbor domains, inspired by the gap gene system in the Drosophila embryo. We find that fine-tuning of the weak repressive interactions to an optimal level can significantly increase temporal stability of the expression patterns, allowing for stable patterns over developmentally relevant times in the absence of morphogen gradients. The numerically determined optimal parameters closely agree with the predictions of the stability theory. By analizing the dynamics of factors characterizing pattern integrity, we trace back the stability enhancement to the emergence of restoring forces, maintaining the pattern in a meta-stable basin. Altogether our results demonstrate that metastable attractors can emerge as a property of stochastic gene expression patterns even without system-wide positional cues, provided that the gene regulatory interactions shaping the pattern are optimally tuned.

Cells are very sensitive to the shear stress (SS). However, undesirable SS is generated during physiological process such as collective cell migration (CCM) and influences the biological processes such as morphogenesis, wound healing and cancer invasion. Despite extensive research devoted to study the stress generation caused by CCM, we still do not fully understand the main cause of SS generation. An attempt is made here to offer some answers to these questions by considering the rearrangement of cell monolayers. The SS generation represents a consequence of natural and forced convection. While forced convection is dependent on cell speed, the natural convection is induced by the gradient of tissue surface tension. The phenomenon is known as the Marangoni effect. The gradient of tissue surface tension induces directed cell spreading from the regions of lower tissue surface tension to the regions of higher tissue surface tension. This directed cell migration is described by the Marangoni flux. The phenomenon has been recognized during the rearrangement of (1) epithelial cell monolayers and (2) mixed cell monolayers made by epithelial and mesenchymal cells. The consequence of the Marangoni effect is an intensive spreading of cancer cells through an epithelium. In this work, a review of existing literature about SS generation caused by CCM is given along with the assortment of published experimental findings, in order to invite experimentalists to test given theoretical considerations in multicellular systems., Comment: 7837 words, 3 figures, 1 table, 64 references

The initiation of directional cell motion requires symmetry breaking that can happen both with or without external stimuli. During cell crawling, forces generated by the cytoskeleton and their transmission through mechanosensitive adhesions to the extracellular substrate play a crucial role. In a recently proposed 1D model (Sens, PNAS 2020), a mechanical feedback loop between force-sensitive adhesions and cell tension was shown to be sufficient to explain spontaneous symmetry breaking and multiple motility patterns through stick-slip dynamics, without the need to account for signaling networks or active polar gels. We extended this model to 2D to study the interplay between cell shape and mechanics during crawling. Through a local force balance along a deformable boundary, we show that the membrane tension coupled with shape change can regulate the spatiotemporal evolution of the stochastic binding of mechanosensitive adhesions. Linear stability analysis identified the unstable parameter regimes where spontaneous symmetry breaking can take place. sing simulations to solve the fully coupled nonlinear system of equations, we show that starting from a randomly perturbed circular shape, this instability can lead to keratocyte-like shapes. Simulations predict that different adhesion kinetics and membrane tension can result in different cell motility modes including gliding, zigzag, rotating, and sometimes chaotic movements. Thus, using a minimal model of cell motility, we identify that the interplay between adhesions and tension can select emergent motility modes., 21 pages, 8 figures

When a founder cell and its progeny divide with incomplete cytokinesis, a network forms in which each intercellular bridge corresponds to a past mitotic event. Networks built in this manner are required for gamete production in many animals, and different species have evolved very different final network topologies. While mechanisms regulating network assembly have been identified in particular organisms, we lack a quantitative framework to understand network assembly and inter-species variability. Motivated by cell networks responsible for oocyte production in invertebrates, where the final topology is typically invariant within each species, we devise a mathematical model for generating cell networks: each node is an oscillator, and after a full cycle, the node produces a daughter to which it remains connected. These cell cycle oscillations on the nodes are transient and coupled via diffusion over the network's edges. By variation of three biologically motivated parameters, our model generates nearly all such networks currently reported across invertebrates. Furthermore, small parameter variations can rationalize cases of within-species variation. Because cell networks outside of the ovary often form less deterministically, we propose generalizations of our model to account for different sources of stochasticity.

Epithelial cells can assemble into cohesive colonies and collectively interact with substrates by generating extracellular forces through focal adhesions. Recently, a molecularly based thermodynamic model, which integrates both the monolayer elasticity and force-mediated focal adhesion formation, has been developed to elucidate the regulation of the cellular force landscape induced by the active epithelial-substrate coupling. However, how epithelial-substrate coupling strength mediate the landscapes of the traction, the cellular displacement, and the focal adhesion distribution in a cohesive monolayer remains unexamined in details. In this work, we follow the procedures by the previous work to re-formulate the free energy of the epithelial-substrate system and obtain the thermodynamic steady-state equations. We then derive a simplified form of the complete equation system, and solve it both semi-analytically and numerically. We find that the parameter which characterizes the epithelial-substrate coupling strength can significantly affect the landscapes of the traction the cellular displacement, and the focal adhesion distribution. We also revisit the "size effect" addressed by previous works and demonstrate that such effect is the natural outcome of a strong epithelial-substrate coupling without introducing any extra factors. For epithelial-substrate coupling which is not strong enough, the currently observed "size effect" does not hold. A scaling law that determines whether the previously observed "size effect" holds is proposed based on our model., 27 pages, 9 figures

Colorectal cancer is a public health problem.Approximately 30 to 50 \% of colorectal tumors are caused by mutations in the KRAS gene.These mutations induce uncontrolled proliferation.To date,There is no approved effective treatment for the mutated KRAS oncogene.Farnesyltransferase (FTI) inhibitors are considered a therapeutic target against the mutated KRAS oncogene.Tipifarnib is a farnesyltransferase inhibitor that was analyzed in a Phase II trial.In the present study, the three-dimensional structure of farnesyltransferase complexed with tipifarnib [1SA4] was used as a basis to exploit the characteristics of tipifarnib.A pharmacophore model was generated based on the structure using the Asinex (Gold and Platinum Collections) database.A total of 299 molecules were obtained after screening.The 299 molecules were anchored to the tipifarnib binding site in the farnesyltransferase crystal structure for docking analysis.During the molecular docking process, the pharmacophore that was modeled, and was used as a constraint to eliminate the molecules that do not satisfy the pharmacophore.Finally, four Hits identified as farnesyltransferase inhibitors for biological tests. Keywords: colorectal cancer, structure-based pharmacophore, molecular docking, KRAS, farnesyltransferase inhibitors, Virtual Screening.

We investigate a nonlinear parabolic reaction-diffusion equation describing the oxygen concentration in encapsulated pancreatic cells with a general core-shell geometry. This geometry introduces a discontinuous diffusion coefficient as the material properties of the core and shell differ. We apply monotone operator theory to show well-posedness of the problem in the strong form. Furthermore, the stationary solutions are unique and asymptotically stable. These results rely on the gradient structure of the underlying PDE.

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies., 22 pages, 16 figures

Cells often migrate on curved surfaces inside the body, such as curved tissues, blood vessels or highly curved protrusions of other cells. Recentin-vitroexperiments provide clear evidence that motile cells are affected by the curvature of the substrate on which they migrate, preferring certain curvatures to others, termed “curvotaxis”. The origin and underlying mechanism that gives rise to this curvature sensitivity are not well understood. Here, we employ a “minimal cell” model which is composed of a vesicle that contains curved membrane protein complexes, that exert protrusive forces on the membrane (representing the pressure due to actin polymerization). This minimal-cell model gives rise to spontaneous emergence of a motile phenotype, driven by a lamellipodia-like leading edge. By systematically screening the behaviour of this model on different types of curved substrates (sinusoidal, cylinder and tube), we show that minimal ingredients and energy terms capture the experimental data. The model recovers the observed migration on the sinusoidal substrate, where cells move along the grooves (minima), while avoiding motion along the ridges. In addition, the model predicts the tendency of cells to migrate circumferentially on convex substrates and axially on concave ones. Both of these predictions are verified experimentally, on several cell types. Altogether, our results identify the minimization of membrane-substrate adhesion energy and binding energy between the membrane protein complexes as key players of curvotaxis in cell migration.

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometre, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b=6000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement., 48 pages, 10 figures

Signal transduction and cell function are governed by the spatiotemporal organization of membrane-associated molecules. Despite significant advances in visualizing molecular distributions by 3D light microscopy, cell biologists still have limited quantitative understanding of the processes implicated in the regulation of molecular signals at the whole cell scale. In particular, complex and transient cell surface morphologies challenge the complete sampling of cell geometry, membrane-associated molecular concentration and activity and the computing of meaningful parameters such as the cofluctuation between morphology and signals. Here, we introduce u-Unwrap3D, a framework to remap arbitrarily complex 3D cell surfaces and membrane-associated signals into equivalent lower dimensional representations. The mappings are bidirectional, allowing the application of image processing operations in the data representation best suited for the task and to subsequently present the results in any of the other representations, including the original 3D cell surface. Leveraging this surface-guided computing paradigm, we track segmented surface motifs in 2D to quantify the recruitment of Septin polymers by blebbing events; we quantify actin enrichment in peripheral ruffles; and we measure the speed of ruffle movement along topographically complex cell surfaces. Thus, u-Unwrap3D provides access to spatiotemporal analyses of cell biological parameters on unconstrained 3D surface geometries and signals., 49 pages, 10 figures

A simple cell model consisting of a catalytic reaction network with intermediate complex formation is numerically studied. As nutrients are depleted, the transition from the exponential growth phase to the growth-arrested dormant phase occurs along with hysteresis and a lag time for growth recovery. This transition is caused by the accumulation of intermediate complexes, leading to the jamming of reactions and the diversification of components. These properties are generic in random reaction networks, as supported by dynamical systems analyses of corresponding mean-field models., 6+6 pages, 3+6 figures

Cell decision making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker–Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate the cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters.

A classic problem in metabolism is that fast-proliferating cells use seemingly wasteful fermentation to generate energy in the presence of sufficient oxygen. This counterintuitive phenomenon, known as overflow metabolism, or the Warburg effect in cancer, is universal across various organisms. Despite extensive research, its origin and function remain unclear. Here, we take Escherichia coli as a typical example and show that overflow metabolism can be understood through growth optimization combined with cell heterogeneity. A model of optimal protein allocation, coupled with heterogeneity in enzyme catalytic rates among cells, quantitatively explains why and how cells make the choice between respiration and fermentation under different nutrient conditions. Our model quantitatively illustrates the growth rate dependence of fermentation flux and enzyme allocation under various perturbations, which is fully validated by experimental results. Our work solves the long-standing puzzle of overflow metabolism and can be broadly used to address heterogeneity-related challenges in metabolism., Maintext: 19 pages, 4 figures. SI: 60 pages, 2 tables, 9 figures

The conditions under which biological cells switch from a static to a motile state are fundamental to the understanding of many healthy and pathological processes. In this paper, we show that even in the presence of a fully symmetric protrusive activity at the cell edges, such a spontaneous transition can result solely from the mechanical interaction of the cell traction forces with an elastic substrate. The loss of symmetry of the traction forces leading to the cell propulsion is rooted in the fact that the surface loading follows the substrate deformation. The bifurcation between the static and motile states is characterized analytically and, considering the measurements performed on two cell types, we show that such an instability can realistically occur on soft in vivo substrates.

This paper presents a mathematical model for tumour-immune response interactions in the perspective of immunotherapy by immune checkpoint inhibitors (ICIs). The model is of the integrodifferential Lotka-Volterra type, in which heterogeneity of the cell populations is taken into account by structuring variables that are continuous internal traits (aka phenotypes) representing a lumped ''aggressiveness'', i.e., for tumour cells, ability to thrive in a viable state under attack by immune cells or drugs-which we propose to identify as a potential of de-differentiation-, and for immune cells, ability to kill tumour cells. We analyse the asymptotic behaviour of the model in the absence of treatment. By means of two theorems, we characterise the limits of the integro-differential system under an a priori convergence hypothesis. We illustrate our results with numerical simulations, which show that our model exemplifies the three Es of immunoediting: elimination, equilibrium, and escape.

Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics. Methods: This report describes MigraR, an intuitive graphical user interface executed from the RStudioTM (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness. Results: MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software. Conclusions: Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced., This work is financed by National Funds through the Portuguese funding agency, FCT- Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020.

Motivated by bacterial chemotaxis and multi-species ecological interactions in heterogeneous environments, we study a general one-dimensional reaction-cross-diffusion system in the presence of spatial heterogeneity in both transport and reaction terms. Under a suitable asymptotic assumption that the transport is slow over the domain, while gradients in the reaction heterogeneity are not too sharp, we study the stability of a heterogeneous steady state approximated by the system in the absence of transport. Using a WKB ansatz, we find that this steady state can undergo a Turing-type instability in subsets of the domain, leading to the formation of localized patterns. The boundaries of the pattern-forming regions are given asymptotically by `local' Turing conditions corresponding to a spatially homogeneous analysis parameterized by the spatial variable. We developed a general open-source code which is freely available, and show numerical examples of this localized pattern formation in a Schnakenberg cross-diffusion system, a Keller-Segel chemotaxis model, and the Shigesada-Kawasaki-Teramoto model with heterogeneous parameters. We numerically show that the patterns may undergo secondary instabilities leading to spatiotemporal movement of spikes, though these remain approximately within the asymptotically predicted localized regions. This theory can elegantly differentiate between spatial structure due to background heterogeneity, from spatial patterns emergent from Turing-type instabilities., Comment: 34 pages, 6 figures

Spatially annotated single-cell datasets provide unprecedented opportunities to dissect cell-cell communication in development and disease. Heterotypic signaling includes interactions between different cell types and is well established in tissue development and spatial organization. Epithelial organization requires several different programs that are tightly regulated. Planar cell polarity is the organization of epithelial cells along the planar axis orthogonal to the apical-basal axis. In this study, we investigate planar cell polarity factors and explore the implications of developmental regulators as malignant drivers. Utilizing cancer systems biology analysis, we derive gene expression network for WNT-ligands (WNT) and their cognate frizzled (FZD) receptors in skin cutaneous melanoma. The profiles supported by unsupervised clustering of multiple-sequence alignments identify ligand-independent signaling and implications for metastatic progression based on the underpinning developmental spatial program. Omics studies and spatial biology connect developmental programs with oncological events and explain key spatial features of metastatic aggressiveness. Dysregulation of prominent planar cell polarity factors such specific representative of the WNT and FZD families in malignant melanoma recapitulates the development program of normal melanocytes but in an uncontrolled and disorganized fashion., Comment: 7 pages, 2 figures

Survival of living tumor cells underlies many influences such as nutrient saturation, oxygen level, drug concentrations or mechanical forces. Data-supported mathematical modeling can be a powerful tool to get a better understanding of cell behavior in different settings. However, under consideration of numerous environmental factors mathematical modeling can get challenging. We present an approach to model the separate influences of each environmental quantity on the cells in a collective manner by introducing the "environmental stress level". It is an immeasurable auxiliary variable, which quantifies to what extent viable cells would get in a stressed state, if exposed to certain conditions. A high stress level can inhibit cell growth, promote cell death and influence cell movement. As a proof of concept, we compare two systems of ordinary differential equations, which model tumor cell dynamics under various nutrient saturations respectively with and without considering an environmental stress level. Particle-based Bayesian inversion methods are used to quantify uncertainties and calibrate unknown model parameters with time resolved measurements of in vitro populations of liver cancer cells. The calibration results of both models are compared and the quality of fit is quantified. While predictions of both models show good agreement with the data, there is indication that the model considering the stress level yields a better fitting. The proposed modeling approach offers a flexible and extendable framework for considering systems with additional environmental factors affecting the cell dynamics.

Many reaction-diffusion models produce travelling wave solutions that can be interpreted as waves of invasion in biological scenarios such as wound healing or tumour growth. These partial differential equation models have since been adapted to describe the interactions between cells and extracellular matrix (ECM), using a variety of different underlying assumptions. In this work, we derive a system of reaction-diffusion equations, with cross-species density-dependent diffusion, by coarse-graining an agent-based, volume-filling model of cell invasion into ECM. We study the resulting travelling wave solutions both numerically and analytically across various parameter regimes. Subsequently, we perform a systematic comparison between the behaviours observed in this model and those predicted by simpler models in the literature that do not take into account volume-filling effects in the same way. Our study justifies the use of some of these simpler, more analytically tractable models in reproducing the qualitative properties of the solutions in some parameter regimes, but it also reveals some interesting properties arising from the introduction of cell and ECM volume-filling effects, where standard model simplifications might not be appropriate., 28 pages, 12 figures

There has been on-going philosophical debate on whether artificial life models, also known as digital organisms, are truly alive. The main difficulty appears to be finding an encompassing and definite definition of life. By examining similarities and differences in recent definitions of life, we define life as "any system with a boundary to confine the system within a definite volume and protect the system from external effects, consisting of a program that is capable of improvisation, able to react and adapt to the environment, able to regenerate parts of it-self or its entirety, with energy system comprises of non-interference sets of secluded reactions for self-sustenance, is considered alive or a living system. Any incomplete system containing a program and can be re-assembled into a living system; thereby, converting the reassembled system for the purpose of the incomplete system, are also considered alive." Using this definition, we argue that digital organisms may not be the boundary case of life even though some digital organisms are not considered alive; thereby, taking the view that some form of digital organisms can be considered alive. In addition, we present an experimental framework based on continuity of the overall system and potential discontinuity of elements within the system for testing future definitions of life.

Movement of cell clusters along extracellular matrices (ECM) during tissue development, wound healing, and early stage of cancer invasion involve various inter-connected migration modes such as: (1) cell movement within clusters, (2) cluster extension (wetting) and compression (de-wetting), and (3) directional cluster movement. It has become increasingly evident that dilational and volumetric viscoelasticity of cell clusters and their surrounding substrate significantly influence these migration modes through physical parameters such as: cell and matrix surface tensions, interfacial tension between cells and substrate, gradients of surface and interfacial tensions, as well as, the accumulation of cell and matrix residual stresses. Inhomogeneous distribution of cell surface tension along migrating cell cluster can appear as a consequence of different strength of cell-cell adhesion contacts and cell contractility between leader and follower cells. While the directional cell migration caused by the matrix stiffness gradient (i.e. durotaxis) has been widely elaborated, the structural changes of matrix surface caused by cell tractions which lead to the generation of the matrix surface tension gradient has not been considered yet. The main goal of this theoretical consideration is to clarify the roles of various physical parameters in collective cell migration based on the formulating biophysical model. This complex phenomenon is discussed on the model systems such as the movement of cell clusters on the collagen I gel matrix by simultaneously reviewing various experimental data with and without cells., 21 pages, 3 figures, 2 tables

Antibodies and humoral memory are key components of the adaptive immune system. We consider and computationally model mechanisms by which humoral memory present at baseline might instead increase infection load; we refer to this effect as EI-HM (enhancement of infection by humoral memory). We first consider antibody dependent enhancement (ADE) in which antibody enhances the growth of the pathogen, typically a virus, and typically at intermediate "Goldilocks" levels of antibody. Our ADE model reproduces ADE in vitro and enhancement of infection in vivo from passive antibody transfer. But notably the simplest implementation of our ADE model never results in EI-HM. Adding complexity, by making the cross-reactive antibody much less neutralizing than the de novo generated antibody or by including a sufficiently strong non-antibody immune response, allows for ADE-mediated EI-HM. We next consider the possibility that cross-reactive memory causes EI-HM by crowding out a possibly superior de novo immune response. We show that, even without ADE, EI-HM can occur when the cross-reactive response is both less potent and "directly" (i.e. independently of infection load) suppressive with regard to the de novo response. In this case adding a non-antibody immune response to our computational model greatly reduces or completely eliminates EI-HM, which suggests that "crowding out" is unlikely to cause substantial EI-HM. Hence, our results provide examples in which simple models give qualitatively opposite results compared to models with plausible complexity. Our results may be helpful in interpreting and reconciling disparate experimental findings, especially from dengue, and for vaccination.

Eukaryotic cells intrinsically change their shape, by changing the composition of their membrane and by restructuring their underlying cytoskeleton. We present here further studies and extensions of a minimal physical model, describing a closed vesicle with mobile curved membrane protein complexes. The cytoskeletal forces describe the protrusive force due to actin polymerization which is recruited to the membrane by the curved protein complexes. We characterize the phase diagrams of this model, as function of the magnitude of the active forces, nearest-neighbor protein interactions and the proteins' spontaneous curvature. It was previously shown that this model can explain the formation of lamellipodia-like flat protrusions, and here we explore the regimes where the model can also give rise to filopodia-like tubular protrusions. We extend the simulation with curved components of both convex and concave species, where we find the formation of complex ruffled clusters, as well as internalized invaginations that resemble the process of endocytosis and macropinocytosis. We alter the force model representing the cytoskeleton to simulate the effects of bundled instead of branched structure, resulting in shapes which resemble filopodia., 28 pages, 15 figure; Supplementary information is available in https://weizmann.box.com/s/5z6aexxk1lmne0b29sf25o451unnj85x (PDF links might be broken). Submitted to Frontiers in Molecular Biosciences and cellular biochemistry special issue "Exploring the Molecular Interplay between the Plasma Membrane and Cytoskeleton during Signal Transduction"

Despite widespread and striking examples of physiological oscillations, their functional role is often unclear. Even glycolysis, the paradigm example of oscillatory biochemistry, has seen questions about its oscillatory function. Here, we take a systems approach to summarize evidence that oscillations play critical physiological roles. Oscillatory behavior enables systems to avoid desensitization, to avoid chronically high and therefore toxic levels of chemicals, and to become more resistant to noise. Oscillation also enables complex physiological systems to reconcile incompatible conditions such as oxidation and reduction, by cycling between them, and to synchronize the oscillations of many small units into one large effect. In pancreatic beta cells, glycolytic oscillations are in synchrony with calcium and mitochondrial oscillations to drive pulsatile insulin release, which is pivotal for the liver to regulate blood glucose dynamics. In addition, oscillation can keep biological time, essential for embryonic development in promoting cell diversity and pattern formation. The functional importance of oscillatory processes requires a rethinking of the traditional doctrine of homeostasis, holding that physiological quantities are maintained at constant equilibrium values, a view that has largely failed us in the clinic. A more dynamic approach will enable us to view health and disease through a new light and initiate a paradigm shift in treating diseases, including depression and cancer. This modern synthesis also takes a deeper look into the mechanisms that create, sustain and abolish oscillatory processes, which requires the language of nonlinear dynamics, well beyond the linearization techniques of equilibrium control theory.

Nanoparticles, such as viruses, can enter cells via endocytosis. During endocytosis, the cell surface wraps around the nanoparticle to effectively eat it. Prior focus has been on how nanoparticle size and shape impacts endocytosis. However, inspired by the noted presence of extracellular vimentin affecting viral and bacteria uptake, as well as the structure of coronaviruses, we construct a computational model in which both the cell-like construct and the virus-like construct contain filamentous protein structures protruding from their surfaces. We then study the impact of these additional degrees of freedom on viral wrapping. We find that cells with an optimal density of filamentous extracellular components (ECCs) are more likely to be infected as they uptake the virus faster and use relatively less cell surface area per individual virus. At the optimal density, the cell surface folds around the virus, and folds are faster and more efficient at wrapping the virus than crumple-like wrapping. We also find that cell surface bending rigidity helps generate folds, as bending rigidity enhances force transmission across the surface. However, changing other mechanical parameters, such as the stretching stiffness of filamentous ECCs or virus spikes, can drive crumple-like formation of the cell surface. We conclude with the implications of our study on the evolutionary pressures of virus-like particles, with a particular focus on the cellular microenvironment that may include filamentous ECCs., 15 pages, 8 figures

Current understanding of arrhythmia mechanisms and design of anti-arrhythmic drug therapies hinges on the assumption that myocytes from the same region of a single heart have similar, if not identical, action potential waveforms and drug responses. On the contrary, recent experiments reveal significant heterogeneity in uncoupled healthy myocytes both from different hearts as well as from identical regions within a single heart. In this work, a methodology is developed for quantifying the individual electrophysiological properties of large numbers of uncoupled cardiomyocytes under ion channel block in terms of the parameters values of a conceptual fast-slow model of electrical excitability. The approach is applied to a population of nearly 500 rabbit ventricular myocytes for which action potential duration (APD) before and after the application of the drug nifedipine was experimentally measured (Lachaud et al., 2022, Cardiovasc. Res.). To this end, drug action is represented by a multiplicative factor to an effective ion conductance, a closed form asymptotic expression for APD is derived and inverted to determine model parameters as functions of APD and dAPD (drug-induced change in APD) for each myocyte. Two free protocol-related quantities are calibrated to experiment using an adaptive-domain procedure based on an original assumption of optimal excitability. The explicit APD expression and the resulting set of model parameter values allow (a) direct evaluation of conditions necessary to maintain fixed APD or dAPD, (b) predictions of the proportion of cells remaining excitable after drug application, (c) predictions of stimulus period dependency and (d) predictions of dose-response curves, the latter being in agreement with additional experimental data., Accepted for publication in Mathematical Medicine and Biology: A Journal of the IMA (ISSN (Online):1477-8602) on 2023-01-15

Biological systems are characterized by the ubiquitous roles of weak, that is, non-covalent molecular interactions, small, often very small, numbers of specific molecules per cell, and Brownian motion. These combine to produce stochastic behaviors at all levels from the molecular and cellular to the behavioral. That said, students are rarely introduced to the ubiquitous role of stochastic processes in biological systems, and how they produce unpredictable behaviors. Here I present the case that they need to be and provide some suggestions as to how it might be approached., 10 pages, 7 embedded figures, 3 text boxes

Bacterial cells are sensitive to a range of external signals used to learn the environment. These incoming external signals are then processed using a Gene Regulatory Network (GRN), exhibiting similarities to modern computing algorithms. An in-depth analysis of gene expression dynamics suggests an inherited Gene Regulatory Neural Network (GRNN) behavior within the GRN that enables the cellular decision-making based on received signals from the environment and neighbor cells. In this study, we extract a sub-network of \textit{Pseudomonas aeruginosa} GRN that is associated with one virulence factor: pyocyanin production as a use case to investigate the GRNN behaviors. Further, using Graph Neural Network (GNN) architecture, we model a single species biofilm to reveal the role of GRNN dynamics on ecosystem-wide decision-making. Varying environmental conditions, we prove that the extracted GRNN computes input signals similar to natural decision-making process of the cell. Identifying of neural network behaviors in GRNs may lead to more accurate bacterial cell activity predictive models for many applications, including human health-related problems and agricultural applications. Further, this model can produce data on causal relationships throughout the network, enabling the possibility of designing tailor-made infection-controlling mechanisms. More interestingly, these GRNNs can perform computational tasks for bio-hybrid computing systems.

Axes of polarity (and primary morphogenetic gradients) are established in the oocyte - early embryo through active transport and localization of maternal factors. It is the oocyte - syncytial embryo of Drosophila (D. melanogaster) that is a model object for studying the molecular machinery of such transport systems. The attention of researchers is focused on the processes of formation, maintenance, and functioning of active transport systems of maternal mRNAs and proteins that are key for early Drosophila embryogenesis. Here we develop an approach for agent-based 3D modeling of the key components of transport by molecular motors (by elements of the cytoskeleton) of the Drosophila oocyte-syncytial embryo. The models were developed using Skeledyne software developed by Odell and Foe [Odell and Foe, 2008]. We start with the results of modeling transport along oriented microtubule (MT) bundles in the oocyte. This is a model of transport systems in the Drosophila oocyte, where three maternal mRNAs (bicoid (bcd), oskar, and gurken) that are key to embryonic polarity are transported along their oriented MT bundles. Then we consider models of oriented MT networks in the volume of a cell (oocyte) generated by a single microtubule organization center (or a pair of the centers). This model reproduces the formation of bcd mRNA intrusions deep into the cytoplasm in the head half of the early syncytial embryo. Finally, we consider models for the active transport of bcd mRNA in a syncytial embryo along a randomized network of many short MT strands. In conclusion, we consider the prospects for the implementation of cytoplasmic fountain flows in the active transport model., 38 pages, 26 figures, in russian

Cells are dynamic systems characterized by temporal variations in biophysical properties such as stiffness and contractility. Recent studies show that the recruitment and release of actin filaments into and out of the cell cortex - a network of proteins underneath the cell membrane - leads to cell stiffening prior to division and softening immediately afterward. In three-dimensional (3D) cell collectives, it is unclear whether the stiffness change during division at the single-cell scale controls the spatial structure and dynamics at the multicellular scale. This is an important question to understand as cell stiffness variations play an important role in tissue spatial organization and cancer progression. Using a minimal 3D model incorporating cell birth, death, and cell-to-cell elastic and adhesive interactions, we investigate the effect of mechanical heterogeneity - variations in individual cell stiffnesses that make up the tumor cell collective - on tumor spatial organization and cell dynamics. We discover that spatial mechanical heterogeneity characterized by a spheroid core composed of stiffer cells and softer cells in the periphery emerge within dense 3D cell collectives, which may be a general feature of multicellular tumor growth. We show that heightened spatial mechanical heterogeneity enhances single-cell dynamics and volumetric tumor growth driven by fluctuations in cell elasticity. Our results could have important implications for understanding how spatiotemporal variations in single-cell stiffness determine tumor growth and spread., 10 pages, 5 figures

A common type of cytoskeletal morphology involves multiple converging microbutubules with their minus ends collected and stabilized by a microtubule organizing center (MTOC) in the interior of the cell. This arrangement enables the ballistic transport of cargo bound to microtubules, both dynein mediated transport towards the MTOC and kinesin mediated transport away from it, interspersed with diffusion for unbound cargo-motor complexes. Spatial and temporal positioning of the MTOC allows for bidirectional transport towards and away from specific organelles and locations within the cell and also the sequestering and subsequent dispersal of dynein transported cargo. The general principles governing dynamics, efficiency and tunability of such transport in the MTOC vicinity is not fully understood. To address this, we develop a one-dimensional model that includes advective transport towards an attractor (such as the MTOC), and diffusive transport that allows particles to reach absorbing boundaries (such as cellular membranes). We calculated the mean first passage time (MFPT) for cargo to reach the boundaries as a measure of the effectiveness of sequestering (large MFPT) and diffusive dispersal (low MFPT). The MFPT experiences a dramatic growth in magnitude, transitioning from a low to high MFPT regime (dispersal to sequestering) over a window of cargo attachment/detachment rates that is close to in vivo values. We find that increasing either the attachment or detachment rate, while fixing the other, can result in optimal dispersal when the attractor is placed asymmetrically. Finally, we describe a rare event regime, where the escape location is exponentially sensitive to the attractor positioning. Our results suggest that structures such as the MTOC allow for the sensitive control of the spatial and temporal features of transport and corresponding function under physiological conditions.

The automated segmentation and tracking of macrophages during their migration are challenging tasks due to their dynamically changing shapes and motions. This paper proposes a new algorithm to achieve automatic cell tracking in time-lapse microscopy macrophage data. First, we design a segmentation method employing space-time filtering, local Otsu's thresholding, and the SUBSURF (subjective surface segmentation) method. Next, the partial trajectories for cells overlapping in the temporal direction are extracted in the segmented images. Finally, the extracted trajectories are linked by considering their direction of movement. The segmented images and the obtained trajectories from the proposed method are compared with those of the semi-automatic segmentation and manual tracking. The proposed tracking achieved 97.4% of accuracy for macrophage data under challenging situations, feeble fluorescent intensity, irregular shapes, and motion of macrophages. We expect that the automatically extracted trajectories of macrophages can provide pieces of evidence of how macrophages migrate depending on their polarization modes in the situation, such as during wound healing., Computers in Biology and Medicine, Volume 153, 106499,(2023)

Morphogenesis, tissue regeneration and cancer invasion involve transitions in tissue morphology. These transitions, caused by collective cell migration (CCM), have been interpreted as active wetting/de-wetting transitions. This phenomenon is considered on model system such as wetting of cell aggregate on rigid substrate which includes cell aggregate movement and isotropic/anisotropic spreading of cell monolayer around the aggregate depending on the substrate rigidity and aggregate size. This model system accounts for the transition between 3D epithelial aggregate and 2D cell monolayer as a product of: (1) tissue surface tension, (2) surface tension of substrate matrix, (3) cell-matrix interfacial tension, (4) interfacial tension gradient, (5) viscoelasticity caused by CCM, and (6) viscoelasticity of substrate matrix. These physical parameters depend on the cell contractility and state of cell-cell and cell matrix adhesion contacts, as well as, the stretching/compression of cellular systems caused by CCM. Despite extensive research devoted to study cell wetting, we still do not understand interplay among these physical parameters which induces oscillatory trend of cell rearrangement. This review focuses on these physical parameters in governing the cell rearrangement in the context of epithelial aggregate wetting.de-wetting, and on the modelling approaches aimed at reproducing and understanding these biological systems. In this context, we do not only review previously-published bio-physics models for cell rearrangement caused by CCM, but also propose new extensions of those models in order to point out the interplay between cell-matrix interfacial tension and epithelial viscoelasticity and the role of the interfacial tension gradient in cell spreading., 25 pages, 2 figures, 1 table

The acoustic contrast factor (ACF) is calculated from the relative density and compressibility differences between a fluid and an object in the fluid. To name but one application, this acoustic contrast can be exploited using acoustophoretic systems to isolate cancer cells from a liquid biopsy, such as a blood sample. Knowing the ACF of a cancer cell represents a crucial step in the design of acoustophoretic systems for this purpose, potentially allowing the isolation of circulating cancer cells without labels or contact. For biological cells the static compressibility is different from the high frequency counterpart relevant for the ACF. In this study, we started by characterizing the ACF of low vs. high metastatic cell lines with known associated differences in phenotypic static E-modulus. The change in the static E-modulus, however, was not reflected in a change of the ACF, prompting a more in depth analysis of the influences on the ACF. We demonstrate that static E-modulus increased biological cells through formaldehyde fixation have an increased ACF. Conversely static E-modulus decreased biological cells treated with actin polymerization inhibitor cytochalasin D have a decreased ACF. Complementing these mechanical tests, a numerical COMSOL model was implemented and used to parametrically explore the effects of cell density, cell density ratios, dynamic compressibility and therefore the dynamic bulk modulus. Collectively the combined laboratory and numerical experiments reveal that a change in the static E-modulus alone might, but does not automatically lead to a change of the dynamic ACF for biological cells. This highlights the need for a multiparametic view of the biophysical basis of the cellular ACF, as well as the challenges in harnessing acoustophoretic systems to isolate circulating cells based on their mechanical properties alone.

Organisms that grow and survive in uncertain environments may need to change their physiological state as the environment changes. When the environment is uncertain, one strategy known as bet-hedging is to make these changes randomly and independently of the environment, to ensure that at least part of the population is well adapted. Organisms that collect information from their environment may also use this information to modulate their changes of physiological states. We review these different strategies and point out parallels with the theory of optimal financial investments.

Normal life activities between cells rely crucially on the homeostasis of the cellular microenvironment, but aging and cancer will upset this balance. In this paper, we introduce the microenvironmental feedback mechanism to the growth dynamics of multicellular organisms, which changes the cellular competitive ability, and thereby regulates the growth of multicellular organisms. We show that the presence of microenvironmental feedback can effectively delay aging, but cancer cells may grow uncontrollably due to the emergence of the tumor microenvironment (TME). We study the effect of the fraction of cancer cells relative to that of senescent cells on the feedback rate of the microenvironment on the lifespan of multicellular organisms, and find that the average lifespan shortened is close to the data for non-Hodgkin lymphoma in Canada from 1980 to 2015. We also investigate how the competitive ability of cancer cells affects the lifespan of multicellular organisms, and reveal that there is an optimal value of the competitive ability of cancer cells allowing the organism to survive longest. Interestingly, the proposed microenvironmental feedback mechanism can give rise to the phenomenon of Parrondo's paradox: when the competitive ability of cancer cells switches between a too high and a too low value, multicellular organisms are able to live longer than in each case individually. Our results may provide helpful clues targeted therapies aimed at TME.