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Stationary clusters of vesicles are a prominent feature of axonal transport, but little is known about their physiological and functional relevance to axonal transport. We investigate the role of vesicle motility characteristics in modulating the formation and lifetimes of such stationary clusters, and their effect on cargo flow. We develop a simulation model describing key features of axonal cargo transport, benchmarking the model against experiments in the Posterior Lateral Mechanosensory (PLM) neurons of C. elegans. Our simulations include multiple microtubule tracks, varied cargo motion states and accounts for dynamic cargo-cargo interactions. Our model also incorporates static obstacles to vesicle transport in the form of microtubule ends, stalled vesicles, and stationary mitochondria. We demonstrate, both in simulations and in an experimental system, that a reduction in reversal rates is associated with a higher proportion of long-lived stationary vesicle clusters and reduced net anterograde transport. Our simulations support the view that stationary clusters function as dynamic reservoirs of cargo vesicles, and reversals aid cargo in navigating obstacles and regulate cargo transport by modulating the proportion of stationary vesicle clusters along the neuronal process.

The adhesion of cells to substrates occurs via integrin clustering and binding to the actin cytoskeleton. Oncogenes modify anchorage-dependent mechanisms in cells during cancer progression. Fluid shear devices provide a label-free, non-invasive way to characterize cell-substrate interactions and heterogeneities in the cell populations. We quantified the critical adhesion strengths of MCF7, MDAMB-231, A549, HPL1D, HeLa, and NIH3T3 cells using a custom fluid shear device. The detachment response was sigmoidal for each cell type. A549 and MDAMB-231 cells had significantly lower adhesion strengths at τ50 than their non-invasive counterparts, HPL1D and MCF7. Detachment dynamics was inversely correlated with cell invasion potentials. A theoretical model, based on τ50 values and the distribution of cell areas on substrates, provided good fits to data from de-adhesion experiments. Quantification of cell tractions, using the Reg-FTTC method on 10 kPa polyacrylamide gels, showed highest values for invasive, MDAMB-231 and A549, cells compared to non-invasive cells. Immunofluorescence studies show differences in vinculin distributions: non-invasive cells have distinct vinculin puncta, whereas invasive cells have more dispersed distributions. The cytoskeleton in non-invasive cells was devoid of well-developed stress fibers, and had thicker cortical actin bundles in the boundary. These correlations in adhesion strengths with cell invasiveness, demonstrated here, may be useful in cancer diagnostics and other pathologies featuring misregulation in adhesion.

Fetal growth is monitored periodically during pregnancy via ultrasound measurements of fetal dimensions such as femur length (FL), head circumference (HC), abdominal circumference (AC), and biparietal diameter (BPD). Multiple growth standards have been published for each of these, which are clinically used to place a fetus on a “growth chart”. These consist of percentile tables varying by weeks of gestation, computed from cohorts of “low-risk” women with healthy lifestyles, living conditions, and clinical parameters. Such charts are prescriptive of ideal growth, but not necessarily descriptive of diverse real-world populations where they may be used. Moreover, they are constructed by pooling all fetal measurements across the cohort, not based on a growth model, and therefore not necessarily predictive of growth of an individual fetus.We show that the Gompertz model, a standard model for constrained growth, with just three intuitive parameters, convincingly fits the growth of fetal ultrasound biometries. Two of these parameters—t0(the inflection time) andc(the rate of decrease of growth rate)—can be treated as universal to all fetuses, while the third parameterAcan be modeled as an overall scale parameter specific to each fetus, which captures the individual variation in growth. On our cohort of 817 pregnant women (“Seethapathy cohort”), we show that not only can the value ofAfor each fetus be inferred from ultrasound data available by the second or the third trimester, but the weight of the baby at delivery can also be predicted with remarkable accuracy using these inferred Gompertz parameters. A model trained on the Seethapathy cohort performs well in estimating the birth weight in an independent validation cohort of 365 women, demonstrating the predictive power of the model. Moreover, we find that deviation from Gompertz-like growth is linked to neonatal complications. Finally, we show that the Gompertz growth curve is a close fit to the standards from WHO, NICHD and INTERGROWTH, with the optimalt0andcclose to that in the Seethapathy cohort. We propose that the Gompertz formula be a basis for future growth standards, with almost all variation described by a single scale parameterA, which can serve either as a descriptor of mean or variance in population, or as a descriptor for growth of an individual fetus. Indeed, the formula is descriptive of typical growth, predictive of future growth, and may be used in prescriptive standards.

Environmental temperature is a key driver of malaria transmission dynamics. Using detailed temperature records from four sites (1800-3200m) in the western Himalaya, we model how temperature regulates parasite development rate (the inverse of the extrinsic incubation period, EIP) in the wild. Using a Briére parametrization of the EIP, combined with Bayesian parameter inference, we study the thermal limits of transmission for avian (P. relictum) and human Plasmodium parasites (P. vivax and P. falciparum) as well as for two malaria-like avian parasites, Haemoproteus and Leucocytozoon. We demonstrate that temperature conditions can substantially alter the incubation period of parasites at high elevation sites (2600-3200m) leading to restricted parasite development or long transmission windows. We then compare estimates of EIP based on measures of mean temperature versus hourly temperatures to show that EIP days vary in cold versus warm environments. We found that human Plasmodium parasites experience a limited transmission window at 2600m. In contrast, for avian Plasmodium transmission was not possible between September to March at 2600m. In addition, temperature conditions suitable for both Haemoproteus and Leucocytozoon transmission were obtained from June to August and in April, at 2600m. Finally, we use temperature projections from a suite of climate models to predict that by 2040, high elevation sites (~ 2600 m) will have a temperature range conducive for malaria transmission, albeit with a limited transmission window. Our study highlights the importance of accounting for fine-scale thermal effects in the expansion of the range of the malaria parasite with global climate change.

We reflect on and review India's COVID-19 pandemic response through the lens of modeling and data. The lessons learned from the Indian context may be beneficial for other countries., Key Messages India has devised innovative strategies to reduce the spread of COVID-19 within the constraints of a low-resource setting. India has also made some questionable policy decisions. Lessons learned from the Indian experience for public health, health care, and data infrastructure can be globally valuable. In this commentary, as a team of public health data scientists engaged in modeling the pandemic since early 2020, we reflect on India's journey over the past 1 year.

Social contact mixing patterns are critical to the transmission of communicable diseases and have been employed to model disease outbreaks including COVID-19. Nonetheless, there is a paucity of studies on contact mixing in low and middle-income countries such as India. Furthermore, mathematical models of disease outbreaks do not account for the temporal nature of social contacts. We conducted a longitudinal study of social contacts in rural north India across three seasons and analysed the temporal differences in contact patterns.A contact diary survey was performed across three seasons from October 2015-16, in which participants were queried on the number, duration, and characteristics of contacts that occurred on the previous day. A total of 8,421 responses from 3,052 respondents (49% females) recorded characteristics of 180,073 contacts. Respondents reported a significantly higher number and duration of contacts in the winter, followed by the summer and the monsoon season (Nemenyi post-hoc, p80%) of contacts that were reported in schools or on public transport involved physical contact.To the best of our knowledge, our study is the first from India to show that contact mixing patterns vary by the time of the year and provides useful implications for pandemic control. Our results can be used to parameterize more accurate mathematical models for prediction of epidemiological trends of infections in rural India.

The course of the COVID-19 pandemic has been driven by several dynamic behavioral, immunological, and viral factors. We used mathematical modeling to explore how the concurrent reopening of schools, increasing levels of hybrid immunity, and the emergence of the Omicron variant affected the trajectory of the pandemic in India, using Andhra Pradesh (pop: 53 million) as an exemplar Indian state.We constructed an age- and contact-structured compartmental model that allows for individuals to proceed through various states depending on whether they have received zero, one, or two doses of the COVID-19 vaccine. We calibrated our model using results from another model (i.e., INDSCI-SIM) as well as available context-specific serosurvey data. The introduction of the Omicron variant is modelled alongside protection gained from hybrid immunity. We predict disease dynamics in the background of hybrid immunity coming from infections and an ongoing vaccination program, given prior levels of seropositivity from earlier waves of infection. We describe the consequences of school reopening on cases across different age-bands, as well as the impact of the Omicron (BA.2) variant.We show the existence of an epidemic peak in India that is strongly related to the value of background seroprevalence. As expected, because children were not vaccinated in India, re-opening schools increases the number of cases in children more than in adults, although in all scenarios, the peak number of active hospitalizations was never greater than 0.45 times the corresponding peak in the Delta wave before schools were reopened. We varied the level of infection induced seropositivity in our model and found the height of the peak associated with schools reopening reduced as background infection-induced seropositivity increased from 20% to 40%. At reported values of seropositivity of 64% from representative surveys done in India, no discernible peak was observed. We also explored counterfactual scenarios regarding the effect of vaccination on hybrid immunity. We found that in the absence of vaccination, even at high levels of seroprevalence (60%), the emergence of the Omicron variant would have resulted in a large rise in cases across all age bands by as much as 1.8 times. We conclude that the presence of high levels of hybrid immunity resulted in fewer cases in the Omicron wave than in the Delta wave.In India, decreasing prevalence of immunologically naïve individuals of all ages was associated with fewer cases reported once schools were reopened. In addition, hybrid immunity, together with the lower intrinsic severity of disease associated with the Omicron variant, contributed to low reported COVID-19 hospitalizations and deaths.World Health Organization, Mphasis.

SUMMARYBackgroundThe course of the COVID-19 pandemic has been driven by several dynamic behavioral, immunological, and viral factors. We used mathematical modeling to explore how the concurrent reopening of schools, increasing levels of hybrid immunity, and the emergence of the Omicron variant have affected the trajectory of the pandemic in India, using the model Indian state of Andhra Pradesh (pop: 53 million).MethodsWe constructed an age- and contact-structured compartmental model that allows for individuals to proceed through various states depending on whether they have received zero, one, or two doses of the COVID-19 vaccine. Our model is calibrated using results from other models as well as available serosurvey data. The introduction of the Omicron variant is modelled alongside protection gained from hybrid immunity. We predict disease dynamics in the background of hybrid immunity coming from infections and well as an ongoing vaccination program, given prior levels of seropositivity from earlier waves of infection. We describe the consequences of school reopening on cases across different age-bands, as well as the impact of the Omicron (BA.2) variant.ResultsWe show the existence of an epidemic peak that is strongly related to the value of background seroprevalence. As expected, because children were not vaccinated in India, re-opening schools increases the number of cases in children more than in adults, although most such cases are asymptomatic or mild. The height of this peak reduced as the background infection-induced seropositivity was increased from 20% to 40%. At reported values of seropositivity of 64%, no discernable peak was seen. We also explore counterfactual scenarios regarding the effect of vaccination on hybrid immunity. We find that in the absence of vaccination, even at such high levels of seroprevalence, the emergence of the Omicron variant would have resulted in a large rise in cases across all age bands. We conclude that the presence of high levels of hybrid immunity thus resulted in relatively fewer cases in the Omicron wave than in the Delta wave.InterpretationIn India, the decreasing prevalence of immunologically naïve individuals of all ages helped reduce the number of cases reported once schools were reopened. In addition, hybrid immunity, together with the lower intrinsic severity of disease associated with the Omicron variant, contributed to low reported COVID-19 hospitalizations and deaths.FundingWorld Health Organization, Mphasis

BackgroundThe development and widespread use of an effective SARS-CoV-2 vaccine could help prevent substantial morbidity and mortality associated with COVID-19 infection and mitigate many of the secondary effects associated with non-pharmaceutical interventions. The limited availability of an effective and licensed vaccine will task policymakers around the world, including in India, with decisions regarding optimal vaccine allocation strategies. Using mathematical modelling we aimed to assess the impact of different age-specific COVID-19 vaccine allocation strategies within India on SARS CoV-2-related mortality and infection.MethodsWe used an age-structured, expanded SEIR model with social contact matrices to assess different age-specific vaccine allocation strategies in India. We used state-specific age structures and disease transmission coefficients estimated from confirmed Indian incident cases of COVID-19 between 28 January and 31 August 2020. Simulations were used to investigate the relative reduction in mortality and morbidity of vaccinate allocation strategies based on prioritizing different age groups, and the interactions of these strategies with several concurrent non-pharmacologic interventions (i.e., social distancing, mandated masks, lockdowns). Given the uncertainty associated with current COVID-19 vaccine development, we also varied several vaccine characteristics (i.e., efficacy, type of immunity conferred, and rollout speed) in the modelling simulations.ResultsIn nearly all scenarios, prioritizing COVID-19 vaccine allocation for older populations (i.e., >60yrs old) led to the greatest relative reduction in deaths, regardless of vaccine efficacy, control measures, rollout speed, or immunity dynamics. However, preferential vaccination of this target group often produced higher total symptomatic infection counts and more pronounced estimates of peak incidence than strategies which targeted younger adults (i.e., 20-40yrs or 40-60yrs) or the general population irrespective of age. Vaccine efficacy, immunity type, target coverage and rollout speed all significantly influenced overall strategy effectiveness, with the time taken to reach target coverage significantly affecting the relative mortality benefit comparative to no vaccination.ConclusionsOur findings support global recommendations to prioritize COVID-19 vaccine allocation for older age groups. Including younger adults in the prioritisation group can reduce overall infection rates, although this benefit was countered by the larger mortality rates in older populations. Ultimately an optimal vaccine allocation strategy will depend on vaccine characteristics, strength of concurrent non-pharmaceutical interventions, and region-specific goals such as reducing mortality, morbidity, or peak incidence.

A combination of intermittent active movement of transient aggregates and a paused state that intervenes between periods of active transport has been proposed to underly the slow, directed transport of soluble proteins in axons. A component of passive diffusion in the axoplasm may also contribute to slow axonal transport, although quantitative estimates of the relative contributions of diffusive and active movement in the slow transport of a soluble protein, and in particular how they might vary across developmental stages, are lacking. Here, we propose and study a model for slow axonal transport, addressing data from bleach-recovery measurements on a small, soluble, protein, Choline Acetyltransferase (ChAT), in thin axons of the lateral chordotonal (lch5) sensory neurons of Drosophila. ChAT is mainly present in soluble form in the axon and catalyses the acetylation of choline at the synapse. It does not form particulate structures in axons and moves at rates characteristic of slow component b (≈ 1-10 mm/day or 0.01-0.1 μm/s). Using our model, which incorporates active transport, paused and diffusive states, we predict bleach recovery and cargo trajectories obtained through kymographs, comparing these to experimental observations at different developmental stages. We show that changes in the diffusive fraction of cargo during these developmental stages dominate bleach recovery and that a combination of active motion with a paused state alone cannot reproduce the data. We compared predictions of the model with results from photoactivation experiments. The importance of the diffusive state in reproducing the bleach recovery signal in the slow axonal transport of small soluble proteins is our central result.STATEMENT OF SIGNIFICANCEWhile the fast axonal transport of cargo in axons is by now well-understood, the nature of slow transport remains controversial. A number of different models having been proposed for slow axonal transport, including models which allow for transitions between an intermittently moving molecular-motor driven state and a stalled state. How mechanisms for slow axonal transport are modulated during development is unexplored. We study a number of different models for slow axonal transport, comparing their predictions to data on transport of the enzyme Choline Acetyltransferase (ChAT) in thin lateral chordotonal (lch5) sensory neurons of Drosophila larva, across developmental stages where flux increases significantly. We show that accounting for changes in the diffusive fraction of cargo during these developmental stages is essential and diffusion cannot be neglected in the modelling of the slow axonal transport of small soluble proteins.

The patterns of the large-scale spatial organization of chromatin in interphase human somatic cells are not random. Such patterns include the radial separation of euchromatin and heterochromatin, the territorial organization of individual chromosomes, the non-random locations of chromosome territories and the differential positioning of the two X chromosomes in female cells. These features of large-scale nuclear architecture follow naturally from the hypothesis that ATP-consuming non-equilibrium processes associated with highly transcribed regions of chromosomes are a source of ‘active’ forces. These forces are in excess of those that arise from Brownian motion. Simulations of model chromosomes that incorporate such activity recapitulate these features. In addition, they reproduce many other aspects of the spatial organization of chromatin at large scales that are known from experiments. Our results, reviewed here, suggest that the distribution of transcriptional activity across chromosomes underlies many aspects of large-scale nuclear architecture that were hitherto believed to be unrelated.

Model approaches to nuclear architecture have traditionally ignored the biophysical consequences of ATP-fueled active processes acting on chromatin. However, transcription-coupled activity is a source of stochastic forces that are substantially larger than the Brownian forces present at physiological temperatures. Here, we describe an approach to large-scale nuclear architecture in metazoans that incorporates cell-type-specific active processes. The model predicts the statistics of positional distributions, shapes, and overlaps of each chromosome. Simulations of the model reproduce common organizing principles underlying large-scale nuclear architecture across human cell nuclei in interphase. These include the differential positioning of euchromatin and heterochromatin, the territorial organization of chromosomes (including both gene-density-based and size-based chromosome radial positioning schemes), the nonrandom locations of chromosome territories, and the shape statistics of individual chromosomes. We propose that the biophysical consequences of the distribution of transcriptional activity across chromosomes should be central to any chromosome positioning code.

Flocks of birds, schools of fish, mixtures of motors and cytoskeletal filaments, swimming bacteria and driven granular media are systems of interacting motile units that exhibit collective behaviour. These can all be described as active matter systems, since each individual unit takes energy from an internal energy depot and transduces it into work performed on the environment. We review a model for cyanobacterial phototaxis, emphasising the differences from other models for collective behaviour in active matter systems. The interactions between individual cells during phototaxis are dominated by mechanical forces mediated by their physical attachments through type IV pili (T4P) and through the production of "slime", a complex mixture of non-diffusible polysaccharides deposited by cells that acts to decrease friction locally. The slime, in particular, adds a component to the interaction that is local in space but non-local in time, perhaps most comparable to the pheromones laid down in ant trails. Our results suggest that the time-delayed component of the interactions between bacteria qualify their description as a novel active system, which we refer to as "damp" active matter., Comment: 7 pages, 4 figures

Cyanobacteria rely on photosynthesis, and thus have evolved complex responses to light. These include phototaxis, the ability of cells to sense light direction and move towards or away from it. Analysis of mutants has demonstrated that phototaxis requires the coordination of multiple photoreceptors and signal transduction networks. The output of these networks is relayed to type IV pili (T4P) that attach to and exert forces on surfaces or other neighboring cells to drive “twitching” or “gliding” motility. This, along with the extrusion of polysaccharides or “slime” by cells, facilitates the emergence of group behavior. We evaluate recent models that describe the emergence of collective colony-scale behavior from the responses of individual, interacting cells. We highlight the advantages of “active matter” approaches in the study of bacterial communities, discussing key differences between emergent behavior in cyanobacterial phototaxis and similar behavior in chemotaxis or quorum sensing.

Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. Here, we use a 9-component, age-stratified, contact-structured epidemiological compartmental model, which we call the INDSCI-SIM model, to analyse the first wave of COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to daily reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions (NPIs), an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05—0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 35% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

COVID-19 testing across India uses a mix of two types of tests. Rapid Antigen Tests (RATs) are relatively inexpensive point-of-care lateral-flow-assay tests, but they are also less sensitive. The reverse-transcriptase polymerase-chain-reaction (RT-PCR) test has close to 100% sensitivity and specificity in a laboratory setting, but delays in returning results, as well as increased costs relative to RATs, may vitiate this advantage. India-wide, about 49% of COVID-19 tests are RATs, but some Indian states, including the large states of Uttar Pradesh (pop. 227.9 million) and Bihar (pop. 121.3 million) use a much higher proportion of such tests. Here we show, using simulations based on epidemiological network models, that the judicious use of RATs can yield epidemiological outcomes comparable to those obtained through RT-PCR-based testing and isolation of positives, provided a few conditions are met. These are (a) that RAT test sensitivity is not too low, (b) that a reasonably large fraction of the population, of order 0.5% per day, can be tested, (c) that those testing positive are isolated for a sufficient duration, and that (d) testing is accompanied by other non-pharmaceutical interventions for increased effectiveness. We assess optimal testing regimes, taking into account test sensitivity and specificity, background seroprevalence and current test pricing. We find, surprisingly, that even 100% RAT test regimes should be acceptable, from both an epidemiological as well as a economic standpoint, provided the conditions outlined above are met., Author summary Using network models, we study optimal ways of combining low sensitivity, relatively inexpensive point-of-care rapid antigen tests for COVID-19 with higher sensitivity but more expensive laboratory RT-PCR tests. We take into account background seroprevalence and current test pricing for such tests in India, finding that even purely rapid antigen test-based regimes can produce the same reduction in overall infections that pure RT-PCR tests are capable of. This is provided one can test at scale and isolate those testing positive effectively, that the sensitivity of the rapid test is not too low and that non-pharmaceutical interventions proceed in parallel for increased effectiveness.

Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. The INDSCI-SIM model is a 9-component, age-stratified, contact-structured compartmental model for COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions, an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient across the country. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05 - 0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 40% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

Molecular motors drive the directed transport of presynaptic vesicles along the narrow axons of nerve cells. Stationary clusters of such vesicles are a prominent feature of axonal transport, but little is known about their physiological and functional relevance. Here, we develop a simulation model describing key features of axonal cargo transport with a view to addressing this question, benchmarking the model against our experiments in the touch neurons of C. elegans. Our simulations provide for multiple microtubule tracks and varied cargo motion states while also incorporating cargo-cargo interactions. Our model also incorporates obstacles to vesicle transport in the form of microtubule ends, stalled vesicles, and stationary mitochondria. We devise computational methodologies to simulate both axonal bleaching and axotomy, showing that our results reproduce the properties of both moving as well as stationary cargo in vivo. Increasing vesicle numbers leads to larger and more long-lived stationary clusters of vesicular cargo. Vesicle clusters are dynamically stable, explaining why they are ubiquitously seen. Modulating the rates of cargo motion-state switching allows cluster lifetimes and flux to be tuned both in simulations and experiments. We demonstrate, both in simulations and in an experimental system, that suppressing reversals leads to larger stationary vesicle clusters being formed while also reducing flux. Our simulation results support the view that the physiological significance of clusters is located in their role as dynamic reservoirs of cargo vesicles, capable of being released or sequestered on demand.

Cells in microbial colonies integrate information across multiple spatial and temporal scales while sensing environmental cues. A number of photosynthetic cyanobacteria respond in a directional manner to incident light, resulting in the phototaxis of individual cells. Colonies of such bacteria exhibit large-scale changes in morphology, arising from cell-cell interactions, during phototaxis. These interactions occur through type IV pili-mediated physical contacts between cells, as well as through the secretion of complex polysaccharides (‘slime’) that facilitates cell motion. Here, we describe a computational model for such collective behaviour in colonies of the cyanobacterium Synechocystis. The model is designed to replicate observations from recent experiments on the emergent response of the colonies to varied light regimes. It predicts the complex colony morphologies that arise as a result. We ask if changes in colony morphology during phototaxis can be used to infer if cells integrate information from multiple light sources simultaneously, or respond to these light sources separately at each instant of time. We find that these two scenarios cannot be distinguished from the shapes of colonies alone. However, we show that tracking the trajectories of individual cyanobacteria provides a way of determining their mode of response. Our model allows us to address the emergent nature of this class of collective bacterial motion, linking individual cell response to the dynamics of colony shape., Author summary Microbial colonies in the wild often consist of large groups of heterogeneous cells that coordinate and integrate information across multiple spatio-temporal scales. We describe a computational model for one such collective behaviour, phototaxis, in colonies of the cyanobacterium Synechocystis that move in response to light. The model replicates experimental observations of the response of cyanobacterial colonies to varied light regimes, and predicts the complex colony morphologies that arise as a result. The results suggest that tracking the trajectories of individual cyanobacteria may provide a way of determining their mode of information integration. Our model allows us to address the emergent nature of this class of collective bacterial motion, linking individual cell response to the large scale dynamics of the colony.

Quenched disorder affects translational and orientational correlations in two-dimensional interacting particle systems. Such disorder always suppresses orientational order in crystalline states. Surprisingly, in fluid phases of particles interacting with a power-law repulsive interaction, increasing the strength of a quenched Gaussian random pinning potential appears to enhance hexatic order locally. We propose that nearby pairs of pinned particles lock in the relative orientation of neighbours around them, propagating hexatic orientational order across larger distances than in the unpinned fluid. We test this idea using Monte Carlo simulations of interacting particles in their fluid phase in two dimensions, where two of the particles are constrained to be permanently pinned at a fixed distance from each other. We use Voronoi tesselations of instantaneous particle configurations to demonstrate that these pinned particles create hexatic neighbourhoods that can extend well beyond the range of their separation. This structuring is enhanced when the particle density is increased and is most prominent in the vicinity of the liquid-solid transition. Testing these ideas experimentally using experiments on 2-dimensional colloidal systems should be feasible.

Snapshots of colloidal particles moving on disordered two-dimensional substrates can be used to extract equal-time many-body correlations in their positions. To understand the systematics of these correlations, we perform Monte Carlo simulations of a two-dimensional model fluid placed in a quenched disordered background. We use configurations generated from these simulations to compute translational and orientational two-point correlations at equal time, concentrating on correlations in local orientational order as a function of density and disorder strength. We calculate both the disorder averaged version of conventional two-point correlation functions for orientational order, as well as the disorder averaged version of a novel correlation function of time-averaged disorder-induced inhomogeneities in local orientation analogous to the Edwards-Anderson correlation function in spin systems. We demonstrate that these correlations can exhibit interesting non-monotonic behavior in proximity to the underlying fluid-solid transition and suggest that this prediction should be experimentally accessible., 9 pages, 5 figures

Vasoplegia observed post cardiopulmonary bypass (CPB) is associated with substantial morbidity, multiple organ failure and mortality. Circulating counts of hematopoietic stem cells (HSCs) and endothelial progenitor cells (EPC) are potential markers of neo-vascularization and vascular repair. However, the significance of changes in the circulating levels of these progenitors in perioperative CPB, and their association with post-CPB vasoplegia, are currently unexplored. We enumerated HSC and EPC counts, via flow cytometry, at different time-points during CPB in 19 individuals who underwent elective cardiac surgery. These 19 individuals were categorized into two groups based on severity of post-operative vasoplegia, a clinically insignificant vasoplegic Group 1 (G1) and a clinically significant vasoplegic Group 2 (G2). Differential changes in progenitor cell counts during different stages of surgery were compared across these two groups. Machine-learning classifiers (logistic regression and gradient boosting) were employed to determine if differential changes in progenitor counts could aid the classification of individuals into these groups. Enumerating progenitor cells revealed an early and significant increase in the circulating counts of CD34+ and CD34+CD133+ hematopoietic stem cells (HSC) in G1 individuals, while these counts were attenuated in G2 individuals. Additionally, EPCs (CD34+VEGFR2+) were lower in G2 individuals compared to G1. Gradient boosting outperformed logistic regression in assessing the vasoplegia grouping based on the fold change in circulating CD 34+ levels. Our findings indicate that a lack of early response of CD34+ cells and CD34+CD133+ HSCs might serve as an early marker for development of clinically significant vasoplegia after CPB.

When embryonic stem cells differentiate, the mechanical properties of their nuclei evolve en route to their terminal state. Measurements of the deformability of cell nuclei in the transitional state that intervenes between the embryonic stem cell state and the differentiation primed state of mouse stem cells, indicate that such nuclei are auxetic i.e. have a negative Poisson’s ratio. We show, using a theoretical model, how this unusual mechanical behaviour results from the coupling between chromatin compaction states and nuclear shape. Our biophysical approach, which treats chromatin as an active polymer system whose mechanics is modulated by nucleosome binding and unbinding, reproduces experimental results while providing new predictions. We discuss ways of testing these predictions. Our model suggests a biophysical interpretation of the epigenetic landscape of stem cells.

We present results from molecular dynamics simulations of a spherically confined neutral polymer in the presence of crowding particles, studying polymer shapes and conformations as a function of the strength of the attraction to the confining wall, solvent quality, and the density of crowders. The conformations of the polymer under good solvent conditions are weakly dependent on crowder particle density, even when the polymer is strongly confined. In contrast, under poor solvent conditions, when the polymer assumes a collapsed conformation when unconfined, it can exhibit transitions to two different adsorbed phases, when either the interaction with the wall or the density of crowder particles is changed. One such transition involves a desorbed collapsed phase change to an adsorbed extended phase as the attraction of the polymer towards the confining wall is increased. Such an adsorbed extended phase can exhibit a second transition to an ordered adsorbed collapsed phase as the crowder particle density is increased. The ordered adsorbed collapsed phase of the polymer differs significantly in its structure from the desorbed collapsed phase. We revisit the earlier understanding of the adsorption of confined polymers on attractive surfaces in light of our results.

Model approaches to nuclear architecture have traditionally ignored the biophysical consequences of ATP-fueled active processes acting on chromatin. However, transcription-coupled activity is a source of stochastic forces that are substantially larger than the Brownian forces present at physiological temperatures. Here, we describe a first-principles approach to large-scale nuclear architecture in metazoans that incorporates cell-type-specific active processes. The model predicts the statistics of positional distributions, shapes and overlaps of each chromosome. Our simulations reproduce common organising principles underlying large-scale nuclear architecture across human cell nuclei in interphase. These include the differential positioning of euchromatin and heterochromatin, the territorial organisation of chromosomes including both gene-density-based and size-based chromosome radial positioning schemes, the non-random locations of chromosome territories and the shape statistics of individual chromosomes. We propose that the biophysical consequences of the distribution of transcriptional activity across chromosomes should be central to any chromosome positioning code.

Changes in cell-substrate adhesion are believed to signal the onset of cancer metastasis, but such changes must be quantified against background levels of intrinsic heterogeneity between cells. Variations in cell-substrate adhesion strengths can be probed through biophysical measurements of cell detachment from substrates upon the application of an external force. Here, we investigate, theoretically and experimentally, the detachment of cells adhered to substrates when these cells are subjected to fluid shear. We present a theoretical framework within which we calculate the fraction of detached cells as a function of shear stress for fast ramps as well as for the decay in the fraction of detached cells at fixed shear stress as a function of time. Using HEK and 3T3 fibroblast cells as experimental model systems, we extract characteristic force scales for cell adhesion as well as characteristic detachment times. We estimate force-scales of ~ 500 pN associated to a single focal contact, and characteristic time-scales of 190 ≤ τ ≤ 350s representing cell-spread-area dependent mean first passage times to the detached state at intermediate values of the shear stress. Variations in adhesion across cell types are especially prominent when cell detachment is probed by applying a time-varying shear stress. These methods can be applied to characterizing changes in cell adhesion in a variety of contexts, including metastasis.

Cyanobacteria are a widely distributed, diverse group of photosynthetic bacteria that exhibit phototaxis, or motion in response to light. Cyanobacteria such asSynechocystissp. secrete a mixture of complex polysaccharides that facilitate cell motion, while their type 4 pili allow them to physically attach to each other. Even though cells can respond individually to light, colonies of such bacteria are observed to move collectively towards the light source in dense finger-like projections. Agent-based models are especially useful in connecting individual cell behaviour with the emergent collective phenomena that arise out of their interactions. We present an agent-based model for cyanobacterial phototaxis that accounts for slime deposition as well as for direct physical links between bacteria, mediated through their type 4 pili. We reproduce the experimentally observed aggregation of cells at the colony boundary as a precursor to finger formation. Our model also describes the changes in colony morphology that occur when the location of the light source is abruptly changed. We find that the overall motion of cells toward light remains relatively unimpaired even if a fraction of them do not sense light, allowing heterogeneous populations to continue to mount a robust collective response to stimuli. Our work suggests that in addition to bio-chemical signalling via diffusible molecules in the context of bacterial quorum-sensing, short-ranged physical interactions may also contribute to collective effects in bacterial motility.

Superconductivity occurs in the proximity of other competing orders in a wide variety of materials. Such competing phases may reveal themselves when superconductivity is locally suppressed by a magnetic field in the core of a vortex. We explore the competition between superconductivity and charge density wave order in the attractive Hubbard model on a square lattice. Using Bogoliubov--de Gennes mean-field theory, we study how vortex structures form and evolve as the magnetic flux is tuned. Each vortex seeds a charge density wave region whose size is determined by the energy cost of the competing phase. The vortices form a lattice whose lattice parameter shrinks with increasing flux. Eventually, their charge-ordered cores overlap, leading to a field-driven coexistence phase exhibiting both macroscopic charge order and superconductivity---a ``supersolid.'' Ultimately, superconductivity disappears via a first-order phase transition into a purely charge-ordered state. We demonstrate that the Hubbard model maps to a strong-coupling field theory with a constant-squared-sum constraint. We argue that such a constraint necessarily leads to ordered-vortex cores and field-driven coexistence. The coexistence phase can be interpreted as a crystalline arrangement of meron defects in a pseudospin description. We construct a phase diagram using ${t}^{\ensuremath{'}}$, the next-nearest-neighbor hopping, to tune the competition between phases.

Steady axonal cargo flow is central to the functioning of healthy neurons. However, a substantial fraction of cargo in axons remains stationary up to several minutes. We examine the transport of precursors of synaptic vesicles (pre-SVs), endosomes and mitochondria in Caenorhabditis elegans touch receptor neurons, showing that stationary cargo are predominantly present at actin-rich regions along the neuronal process. Stationary vesicles at actin-rich regions increase the propensity of moving vesicles to stall at the same location, resulting in traffic jams arising from physical crowding. Such local traffic jams at actin-rich regions are likely to be a general feature of axonal transport since they also occur in Drosophila neurons. Repeated touch stimulation of C. elegans reduces the density of stationary pre-SVs, indicating that these traffic jams can act as both sources and sinks of vesicles. This suggests that vesicles trapped in actin-rich regions are functional reservoirs that may contribute to maintaining robust cargo flow in the neuron. A video abstract of this article can be found at: Video S1; Video S2.

Steady axonal cargo flow is central to the functioning of healthy neurons. However, a substantial fraction of cargo in axons remains stationary across a broad distribution of times. We examine the transport of pre-synaptic vesicles (pre-SVs), endosomes and mitochondria in C. elegans touch receptor neurons (TRNs), showing that stalled cargo are predominantly present at actin-rich regions along the neuronal process. Cargo stalled at actin-rich regions increase the propensity of moving cargo to stall at the same location, resulting in traffic jams. Such local traffic jams at actin-rich regions are likely to be a general feature of axonal transport since they occur in Drosophila neurons as well. These traffic jams can act as both sources and sinks of vesicles. We propose that they act as functional reservoirs that contribute to maintaining robust cargo flow in the neuron.

Chromosomes within eukaryotic cell nuclei at interphase are not positioned at random, since gene-rich chromosomes are predominantly found towards the interior of the cell nucleus across a number of cell types. The physical mechanisms that could drive and maintain the spatial segregation of chromosomes based on gene density are unknown. Here, we identify a mechanism for such segregation, showing that the territorial organization of chromosomes, another central feature of nuclear organization, emerges naturally from our model. Our computer simulations indicate that gene density-dependent radial segregation of chromosomes arises as a robust consequence of differences in non-equilibrium activity across chromosomes. Arguing that such differences originate in the inhomogeneous distribution of ATP-dependent chromatin remodeling and transcription machinery on each chromosome, we show that a variety of non-random positional distributions emerge through the interplay of such activity, nuclear shape and specific interactions of chromosomes with the nuclear envelope. Results from our model are in reasonable agreement with experimental data and we make a number of predictions that can be tested in experiments.

Stem cells integrate signals from the microenvironment to generate lineage-specific gene expression programs upon differentiation. Undifferentiated cell nuclei are easily deformable, with an active transcriptome, whereas differentiated cells have stiffer nuclei and condensed chromatin. Chromatin organization in the stem cell state is known to be highly dynamic but quantitative characterizations of its plasticity are lacking. Using fluorescence imaging, we study the spatio-temporal dynamics of nuclear architecture and chromatin compaction in mouse embryonic stem (ES) cells and differentiated states. Individual ES cells exhibit a relatively narrow variation in chromatin compaction, whereas primary mouse embryonic fibroblasts (PMEF) show broad distributions. However, spatial correlations in chromatin compaction exhibit an emergent length scale in PMEFs, although they are unstructured and longer ranged in ES cells. We provide evidence for correlated fluctuations with large amplitude and long intrinsic timescales, including an oscillatory component, in both chromatin compaction and nuclear area in ES cells. Such fluctuations are largely frozen in PMEF. The role of actin and Lamin A/C in modulating these fluctuations is described. A simple theoretical formulation reproduces the observed dynamics. Our results suggest that, in addition to nuclear plasticity, correlated spatio-temporal structural fluctuations of chromatin in undifferentiated cells characterize the stem cell state.

Recent measurements of the distribution of end-to-end distance in short DNA molecules infer cooperative stretching fluctuations. The assumptions underlying the analysis can be questioned if transient, thermally induced defects producing a localized decrease in bending stiffness are present in thermal equilibrium, such as regions in which DNA melts locally (bubbles), sustains large-angle bends (kinks), or can locally transform into an alternative (S-DNA) state. We study a generalized discrete worm-like chain model for DNA, capable of describing these experiments, showing that the model yields accurate fits to available experimental data. Our results indicate that DNA bending arising from such localized defects, rather than solely stretching, can be an equal contributor to end-to-end distance fluctuations for 35-bp DNA and contributes nontrivially to such fluctuations at all scales below the persistence length. The analysis suggests that such fluctuations should exhibit a scale-dependent cooperativity, specifically relevant in determining the behavior of short chains, but which saturates rapidly to a length-independent value for longer DNA, to ensure a consistent physical description of DNA across multiple scales. Our approach provides a minimal, yet accurate, coarse-grained description of DNA at the subpersistence length scales of current experimental interest.

Condensed matter systems, when driven far from equilibrium, often exhibit a far more varied set of phases than their equilibrium counterparts. The existence of non-equilibrium analogs of ‘solids’ and ‘liquids’ has been demonstrated earlier in the context of models for driven disordered vortex lattices in superconductors. Here we study the effects of a structural (polymorphic) transition in a driven two-dimensional crystal placed in a quenched random background. Such a polymorphic crystal is shown to exhibit a complex sequence of unusual dynamical phases as the external drive is varied, including some which have no analog in the undriven pure system. We propose that such states should be accessible in experiments.

Several recent imaging experiments access the equilibrium density profiles of interacting particles confined to a two-dimensional substrate. When these particles are in a fluid phase, we show that such data yields precise information regarding substrate disorder as reflected in one-point functions and two-point correlations of the fluid. Using Monte Carlo simulations and replica generalizations of liquid state theories, we extract unusual two-point correlations of time-averaged density inhomogeneities induced by disorder. Distribution functions such as these have not hitherto been measured but should be experimentally accessible., 10 pages revtex 4 figures

We discuss the effects of a surface on the vortex system in a layered superconductor with vanishingly small Josephson coupling. Within a London theory, we derive the modified pancake vortex interaction in samples with a finite number of layers. We discuss the implications of these modifications for the zero-field transition and for the melting transition in finite fields formulated within a substrate model [Phys. Rev. Lett. 84 (2000) 2698]. Close to the surface, the lattice becomes unstable below the bulk thermodynamic melting temperature. We analyze the resulting surface-induced melting using density functional theory.

We study muon-spin rotation (mu SR) spectra in the mixed phase of highly anisotropic layered superconductors, specifically Bi2+xSr2-xCaCu2O8+delta (BSCCO), by modeling the fluid and solid phases of pancake Vortices using liquid-state and density functional methods. The role of thermal fluctuations in causing motional narrowing of mu SR line shapes is quantified in terms of a first-principles theory of the flux-lattice melting transition. The effects of random point pinning are investigated using a replica treatment of liquid-state correlations and a replicated density functional theory. Our results indicate that motional narrowing in the pure system, although substantial, cannot account for the remarkably small linewidths obtained experimentally at relatively high fields and low temperatures. We find that satisfactory agreement with the mu SR data for BSCCO in this regime can be obtained through the ansatz that this ''phase'' is characterized by frozen short-range positional correlations reflecting the structure of the liquid just above the melting transition. This proposal is consistent with recent suggestions of a ''pinned liquid'' or ''glassy'' state of pancake Vortices in the presence of pinning disorder. Our results for the high-temperature liquid phase indicate that measurable linewidths may be obtained in this phase as a consequence of density inhomogeneities induced by the pinning disorder. The results presented here comprise a unified, first-principles theoretical treatment of mu SR spectra in highly anisotropic layered superconductors in terms of a controlled set of approximations. [S0163-1829(99)08033-9].