Molecular Mechanism of Cytokinesis.

Division of amoebas, fungi, and animal cells into two daughter cells at the end of the cell cycle depends on a common set of ancient proteins, principally actin filaments and myosin-II motors. Anillin, formins, IQGAPs, and many other proteins regulate the assembly of the actin filaments into a contractile ring positioned between the daughter nuclei by different mechanisms in fungi and animal cells. Interactions of myosin-II with actin filaments produce force to assemble and then constrict the contractile ring to form a cleavage furrow. Contractile rings disassemble as they constrict. In some cases, knowledge about the numbers of participating proteins and their biochemical mechanisms has made it possible to formulate molecularly explicit mathematical models that reproduce the observed physical events during cytokinesis by computer simulations.

Dynamically adjusted cell fate decisions and resilience to mutant invasion during steady-state hematopoiesis revealed by an experimentally parameterized mathematical model.

A major next step in hematopoietic stem cell (HSC) biology is to enhance our quantitative understanding of cellular and evolutionary dynamics involved in undisturbed hematopoiesis. Mathematical models have been and continue to be key in this respect, and are most powerful when parameterized experimentally and containing sufficient biological complexity. In this paper, we use data from label propagation experiments in mice to parameterize a mathematical model of hematopoiesis that includes homeostatic control mechanisms as well as clonal evolution. We find that nonlinear feedback control can drastically change the interpretation of kinetic estimates at homeostasis. This suggests that short-term HSC and multipotent progenitors can dynamically adjust to sustain themselves temporarily in the absence of long-term HSCs, even if they differentiate more often than they self-renew in undisturbed homeostasis. Additionally, the presence of feedback control in the model renders the system resilient against mutant invasion. Invasion barriers, however, can be overcome by a combination of age-related changes in stem cell differentiation and evolutionary niche construction dynamics based on a mutant-associated inflammatory environment. This helps us understand the evolution of e.g., TET2 or DNMT3A mutants, and how to potentially reduce mutant burden.

Challenges of accurately estimating sex-biased admixture from X chromosomal and autosomal ancestry proportions.

Sex-biased admixture can be inferred from ancestry-specific proportions of X chromosome and autosomes. In a paper published in the American Journal of Human Genetics, Micheletti et al.1 used this approach to quantify male and female contributions following the transatlantic slave trade. Using a large dataset from 23andMe, they concluded that African and European contributions to gene pools in the Americas were much more sex biased than previously thought. We show that the reported extreme sex-specific contributions can be attributed to unassigned genetic ancestry as well as the limitations of simple models of sex-biased admixture. Unassigned ancestry proportions in the study by Micheletti et al. ranged from ∼1% to 21%, depending on the type of chromosome and geographic region. A sensitivity analysis illustrates how this unassigned ancestry can create false patterns of sex bias and that mathematical models are highly sensitive to slight sampling errors when inferring mean ancestry proportions, making confidence intervals necessary. Thus, unassigned ancestry and the sensitivity of the models effectively prohibit the interpretation of estimated sex biases for many geographic regions in Micheletti et al. Furthermore, Micheletti et al. assumed models of a single admixture event. Using simulations, we find that violations of demographic assumptions, such as subsequent gene flow and/or sex-specific assortative mating, may have confounded the analyses of Micheletti et al., but unassigned ancestry was likely the more important confounding factor. Our findings underscore the importance of using complete ancestry information, sufficiently large sample sizes, and appropriate models when inferring sex-biased patterns of demography. This Matters Arising paper is in response to Micheletti et al.,1 published in American Journal of Human Genetics. See also the response by Micheletti et al.,2 published in this issue.

Projections of human kinship for all countries.

Demographers have long attempted to project future changes in the size and composition of populations, but have ignored what these processes will mean for the size, composition, and age distribution of family networks. Kinship structures matter because family solidarity-a crucial source of informal care for millions of people around the world-is conditional on kin being alive. Here, we present innovative projections of biological kin for the 1950 to 2100 period and discuss what they imply for the availability of informal care. Overall, we project that the number of living kin for individuals will decline dramatically worldwide. While a 65-yo woman in 1950 could expect to have 41 living kin, a 65-yo woman in 2095 is projected to have just 25 [18.8 to 34.7] relatives (lower and upper 80% projection intervals). This represents a 38% [15 to 54] global decline. The composition of family networks is also expected to change, with the numbers of living grandparents and great-grandparents markedly increasing, and the numbers of cousins, nieces and nephews, and grandchildren declining. Family networks will age considerably, as we project a widening age gap between individuals and their kin due to lower and later fertility and longer lifespans. In Italy, for example, the average age of a grandmother of a 35-yo woman is expected to increase from 77.9 y in 1950 to 87.7 y [87.1 to 88.5] in 2095. The projected changes in kin supply will put pressure on the already stretched institutional systems of social support, as more individuals age with smaller and older family networks.

Network switches and their role in circadian clocks.

Circadian rhythms are generated by complex interactions among genes and proteins. Self-sustained ∼24 h oscillations require negative feedback loops and sufficiently strong nonlinearities that are the product of molecular and network switches. Here, we review common mechanisms to obtain switch-like behavior, including cooperativity, antagonistic enzymes, multisite phosphorylation, positive feedback, and sequestration. We discuss how network switches play a crucial role as essential components in cellular circadian clocks, serving as integral parts of transcription-translation feedback loops that form the basis of circadian rhythm generation. The design principles of network switches and circadian clocks are illustrated by representative mathematical models that include bistable systems and negative feedback loops combined with Hill functions. This work underscores the importance of negative feedback loops and network switches as essential design principles for biological oscillations, emphasizing how an understanding of theoretical concepts can provide insights into the mechanisms generating biological rhythms.

Monitoring Progress Towards the Elimination of Hepatitis C as a Public Health Threat in Norway: A Modelling Study Among People Who Inject Drugs and Immigrants.

BACKGROUND: The global incidence target for the elimination of hepatitis C among people who inject drugs (PWID) is <2/100. In Norway, the hepatitis C epidemic is concentrated in PWID. Immigrants are the second most important risk group for chronic infection. We modelled the incidence of hepatitis C among active PWID, and the prevalence of chronic infection among active PWID, ex-PWID, and immigrants in Norway to 2022. METHODS: We built a stochastic compartmental model, which was informed using data from national data sources, literature, and expert opinion. We report median values with 95% credible intervals (CrI). RESULTS: The model estimated 30 (95% Crl, 13-52) new infections among active PWID in 2022, or 0.37/100 (95% Crl, 0.17-0.65), down from a peak of 726 (95% Crl, 506-1067) in 2000. Across all groups, the model estimated 3202 (95% Crl, 1273-6601) chronically infected persons in 2022. Results were robust in sensitivity analyses. CONCLUSIONS: Norway provides an example of the feasibility of hepatitis C elimination in a setting with a concentrated epidemic, high coverage of harm reduction services, and no treatment restrictions. Continued momentum is needed to further reduce the transmission and burden of hepatitis C in Norway.

New Tools and Nuanced Interventions to Accelerate Achievement of the 2030 Roadmap for Neglected Tropical Diseases.

The World Health Organization roadmap for neglected tropical diseases (NTDs) sets out ambitious targets for disease control and elimination by 2030, including 90% fewer people requiring interventions against NTDs and the elimination of at least 1 NTD in 100 countries. Mathematical models are an important tool for understanding NTD dynamics, optimizing interventions, assessing the efficacy of new tools, and estimating the economic costs associated with control programs. As NTD control shifts to increased country ownership and programs progress toward disease elimination, tailored models that better incorporate local context and can help to address questions that are important for decision-making at the national level are gaining importance. In this introduction to the supplement, New Tools and Nuanced Interventions to Accelerate Achievement of the 2030 Roadmap for Neglected Tropical Diseases, we discuss current challenges in generating more locally relevant models and summarize how the articles in this supplement present novel ways in which NTD modeling can help to accelerate achievement and sustainability of the 2030 targets.

Accelerating Progress Towards the 2030 Neglected Tropical Diseases Targets: How Can Quantitative Modeling Support Programmatic Decisions?

Over the past decade, considerable progress has been made in the control, elimination, and eradication of neglected tropical diseases (NTDs). Despite these advances, most NTD programs have recently experienced important setbacks; for example, NTD interventions were some of the most frequently and severely impacted by service disruptions due to the coronavirus disease 2019 (COVID-19) pandemic. Mathematical modeling can help inform selection of interventions to meet the targets set out in the NTD road map 2021-2030, and such studies should prioritize questions that are relevant for decision-makers, especially those designing, implementing, and evaluating national and subnational programs. In September 2022, the World Health Organization hosted a stakeholder meeting to identify such priority modeling questions across a range of NTDs and to consider how modeling could inform local decision making. Here, we summarize the outputs of the meeting, highlight common themes in the questions being asked, and discuss how quantitative modeling can support programmatic decisions that may accelerate progress towards the 2030 targets.

The Impact of Rapid Drug Susceptibility Tests on Gonorrhea Burden and the Life Span of Antibiotic Treatments: A Modeling Study Among Men Who Have Sex With Men in the United States.

Rapid point-of-care tests that diagnose gonococcal infections and identify susceptibility to antibiotics enable individualized treatment. This could improve patient outcomes and slow the emergence and spread of antibiotic resistance. However, little is known about the long-term impact of such diagnostics on the burden of gonorrhea and the effective life span of antibiotics. We used a mathematical model of gonorrhea transmission among men who have sex with men in the United States to project the annual rate of reported gonorrhea cases and the effective life span of ceftriaxone, the recommended antibiotic for first-line treatment of gonorrhea, as well as 2 previously recommended antibiotics, ciprofloxacin and tetracycline, when a rapid drug susceptibility test that estimates susceptibility to ciprofloxacin and tetracycline is available. The use of a rapid drug susceptibility test with ≥50% sensitivity and ≥95% specificity, defined in terms of correct ascertainment of drug susceptibility and nonsusceptibility status, could increase the combined effective life span of ciprofloxacin, tetracycline, and ceftriaxone by at least 2 years over 25 years of simulation. If test specificity is imperfect, however, the increase in the effective life span of antibiotics is accompanied by an increase in the rate of reported gonorrhea cases even under perfect sensitivity.

Rho of Plants patterning: linking mathematical models and molecular diversity.

ROPs (Rho of Plants) are plant specific small GTPases involved in many membrane patterning processes and play important roles in the establishment and communication of cell polarity. These small GTPases can produce a wide variety of patterns, ranging from a single cluster in tip-growing root hairs and pollen tubes to an oriented stripe pattern controlling protoxylem cell wall deposition. For an understanding of what controls these various patterns, models are indispensable. Consequently, many modelling studies on small GTPase patterning exist, often focusing on yeast or animal cells. Multiple patterns occurring in plants, however, require the stable co-existence of multiple active ROP clusters, which does not occur with the most common yeast/animal models. The possibility of such patterns critically depends on the precise model formulation. Additionally, different small GTPases are usually treated interchangeably in models, even though plants possess two types of ROPs with distinct molecular properties, one of which is unique to plants. Furthermore, the shape and even the type of ROP patterns may be affected by the cortical cytoskeleton, and cortex composition and anisotropy differ dramatically between plants and animals. Here, we review insights into ROP patterning from modelling efforts across kingdoms, as well as some outstanding questions arising from these models and recent experimental findings.

A simplified longitudinal model for the development of Type 2 Diabetes Mellitus.

Obesity and diabetes are a progressively more and more deleterious hallmark of modern, well fed societies. In order to study the potential impact of strategies designed to obviate the pathological consequences of detrimental lifestyles, a model for the development of Type 2 diabetes geared towards large population simulations would be useful. The present work introduces such a model, representing in simplified fashion the interplay between average glycemia, average insulinemia and functional beta-cell mass, and incorporating the effects of excess food intake or, conversely, of physical activity levels. Qualitative properties of the model are formally established and simulations are shown as examples of its use.

A Mathematical Model of Diel Activity and Long Time Survival in Phototrophic Mixed-Species Subaerial Biofilms.

Subaerial biofilms (SAB) are intricate microbial communities living on terrestrial surfaces, of interest in a variety of contexts including cultural heritage preservation, microbial ecology, biogeochemical cycling, and biotechnology. Here we propose a mathematical model aimed at better understanding the interplay between cyanobacteria and heterotrophic bacteria, common microbial SAB constituents, and their mutual dependence on local environmental conditions. SABs are modeled as thin mixed biofilm-liquid water layers sitting on stone. A system of ordinary differential equations regulates the dynamics of key SAB components: cyanobacteria, heterotrophs, polysaccharides and decayed biomass, as well as cellular levels of organic carbon, nitrogen and energy. These components are interconnected through a network of energetically dominant metabolic pathways, modeled with limitation terms reflecting the impact of biotic and abiotic factors. Daily cylces of temperature, humidity, and light intensity are considered as input model variables that regulate microbial activity by influencing water availability and metabolic kinetics. Relevant physico-chemical processes, including pH regulation, further contribute to a description of the SAB ecology. Numerical simulations explore the dynamics of SABs in a real-world context, revealing distinct daily activity periods shaped by water activity and light availability, as well as longer time scale survivability conditions. Results also suggest that heterotrophs could play a substantial role in decomposing non-volatile carbon compounds and regulating pH, thus influencing the overall composition and stability of the biofilm.

Genetic parameters and genome-wide association studies for mozzarella and milk production traits, lactation length, and lactation persistency in Murrah buffaloes.

Genetic and genomic analyses of longitudinal traits related to milk production efficiency are paramount for optimizing water buffaloes breeding schemes. Therefore, this study aimed to (1) compare single-trait random regression models under a single-step genomic BLUP setting based on alternative covariance functions (i.e., Wood, Wilmink, and Ali and Schaeffer) to describe milk (MY), fat (FY), protein (PY), and mozzarella (MZY) yields, fat-to-protein ratio (FPR), somatic cell score (SCS), lactation length (LL), and lactation persistency (LP) in Murrah dairy buffaloes (Bubalus bubalis); (2) combine the best functions for each trait under a multiple-trait framework; (3) estimate time-dependent SNP effects for all the studied longitudinal traits; and (4) identify the most likely candidate genes associated with the traits. A total of 323,140 test-day records from the first lactation of 4,588 Murrah buffaloes were made available for the study. The model included the average curve of the population nested within herd-year-season of calving, systematic effects of number of milkings per day, and age at first calving as linear and quadratic covariates, and additive genetic, permanent environment, and residual as random effects. The Wood model had the best goodness of fit based on the deviance information criterion and posterior model probabilities for all traits. Moderate heritabilities were estimated over time for most traits (0.30 ± 0.02 for MY; 0.26 ± 0.03 for FY; 0.45 ± 0.04 for PY; 0.28 ± 0.05 for MZY; 0.13 ± 0.02 for FPR; and 0.15 ± 0.03 for SCS). The heritability estimates for LP ranged from 0.38 ± 0.02 to 0.65 ± 0.03 depending on the trait definition used. Similarly, heritabilities estimated for LL ranged from 0.10 ± 0.01 to 0.14 ± 0.03. The genetic correlation estimates across days in milk (DIM) for all traits ranged from -0.06 (186-215 DIM for MY-SCS) to 0.78 (66-95 DIM for PY-MZY). The SNP effects calculated for the random regression model coefficients were used to estimate the SNP effects throughout the lactation curve (from 5 to 305 d). Numerous relevant genomic regions and candidate genes were identified for all traits, confirming their polygenic nature. The candidate genes identified contribute to a better understanding of the genetic background of milk-related traits in Murrah buffaloes and reinforce the value of incorporating genomic information in their breeding programs.

Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis).

The objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: -176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.

Predictive Models of Life Span in Old Female Mice Based on Immune, Redox, and Behavioral Parameters.

The development of mathematical models capable of predicting the lifespan of animals is growing. However, there are no studies that compare the predictive power of different sets of parameters depending on the age of the animals. The aim of the present study is to test whether mathematical models for life span prediction developed in adult female mice based on immune, redox, and behavioral parameters can predict life span in old animals and to develop new models in old mice. For this purpose, 29 variables, including parameters of immune function, redox state, and behavioral ones, were evaluated in old female Swiss mice (80 ± 4 weeks). Life span was registered when they died naturally. Firstly, we observed that the models developed in adults were not able to accurately predict the life span of old mice. Therefore, the immunity (adjusted R2 = 73.6%), redox (adjusted R2 = 46.5%), immunity-redox (adjusted R2 = 96.4%), and behavioral (adjusted R2 = 67.9%) models were developed in old age. Finally, the models were validated in another batch of mice. The developed models in old mice show certain similarities to those in adults but include different immune, redox, and behavioral markers, which highlights the importance of age in the prediction of life span.

Efficient Estimates of Surface Diffusion Parameters for Spatio-Temporally Resolved Virus Replication Dynamics.

Advanced methods of treatment are needed to fight the threats of virus-transmitted diseases and pandemics. Often, they are based on an improved biophysical understanding of virus replication strategies and processes in their host cells. For instance, an essential component of the replication of the hepatitis C virus (HCV) proceeds under the influence of nonstructural HCV proteins (NSPs) that are anchored to the endoplasmatic reticulum (ER), such as the NS5A protein. The diffusion of NSPs has been studied by in vitro fluorescence recovery after photobleaching (FRAP) experiments. The diffusive evolution of the concentration field of NSPs on the ER can be described by means of surface partial differential equations (sufPDEs). Previous work estimated the diffusion coefficient of the NS5A protein by minimizing the discrepancy between an extended set of sufPDE simulations and experimental FRAP time-series data. Here, we provide a scaling analysis of the sufPDEs that describe the diffusive evolution of the concentration field of NSPs on the ER. This analysis provides an estimate of the diffusion coefficient that is based only on the ratio of the membrane surface area in the FRAP region to its contour length. The quality of this estimate is explored by a comparison to numerical solutions of the sufPDE for a flat geometry and for ten different 3D embedded 2D ER grids that are derived from fluorescence z-stack data of the ER. Finally, we apply the new data analysis to the experimental FRAP time-series data analyzed in our previous paper, and we discuss the opportunities of the new approach.

Assessing the impacts of COVID-19 vaccination programme's timing and speed on health benefits, cost-effectiveness, and relative affordability in 27 African countries.

BACKGROUND: The COVID-19 vaccine supply shortage in 2021 constrained roll-out efforts in Africa while populations experienced waves of epidemics. As supply improves, a key question is whether vaccination remains an impactful and cost-effective strategy given changes in the timing of implementation. METHODS: We assessed the impact of vaccination programme timing using an epidemiological and economic model. We fitted an age-specific dynamic transmission model to reported COVID-19 deaths in 27 African countries to approximate existing immunity resulting from infection before substantial vaccine roll-out. We then projected health outcomes (from symptomatic cases to overall disability-adjusted life years (DALYs) averted) for different programme start dates (01 January to 01 December 2021, n = 12) and roll-out rates (slow, medium, fast; 275, 826, and 2066 doses/million population-day, respectively) for viral vector and mRNA vaccines by the end of 2022. Roll-out rates used were derived from observed uptake trajectories in this region. Vaccination programmes were assumed to prioritise those above 60 years before other adults. We collected data on vaccine delivery costs, calculated incremental cost-effectiveness ratios (ICERs) compared to no vaccine use, and compared these ICERs to GDP per capita. We additionally calculated a relative affordability measure of vaccination programmes to assess potential nonmarginal budget impacts. RESULTS: Vaccination programmes with early start dates yielded the most health benefits and lowest ICERs compared to those with late starts. While producing the most health benefits, fast vaccine roll-out did not always result in the lowest ICERs. The highest marginal effectiveness within vaccination programmes was found among older adults. High country income groups, high proportions of populations over 60 years or non-susceptible at the start of vaccination programmes are associated with low ICERs relative to GDP per capita. Most vaccination programmes with small ICERs relative to GDP per capita were also relatively affordable. CONCLUSION: Although ICERs increased significantly as vaccination programmes were delayed, programmes starting late in 2021 may still generate low ICERs and manageable affordability measures. Looking forward, lower vaccine purchasing costs and vaccines with improved efficacies can help increase the economic value of COVID-19 vaccination programmes.

Tracing the footsteps of autophagy in computational biology.

Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids' inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.

The role of migration in mutant dynamics in fragmented populations.

Mutant dynamics in fragmented populations have been studied extensively in evolutionary biology. Yet, open questions remain, both experimentally and theoretically. Some of the fundamental properties predicted by models still need to be addressed experimentally. We contribute to this by using a combination of experiments and theory to investigate the role of migration in mutant distribution. In the case of neutral mutants, while the mean frequency of mutants is not influenced by migration, the probability distribution is. To address this empirically, we performed in vitro experiments, where mixtures of GFP-labelled ("mutant") and non-labelled ("wid-type") murine cells were grown in wells (demes), and migration was mimicked via cell transfer from well to well. In the presence of migration, we observed a change in the skewedness of the distribution of the mutant frequencies in the wells, consistent with previous and our own model predictions. In the presence of de novo mutant production, we used modelling to investigate the level at which disadvantageous mutants are predicted to exist, which has implications for the adaptive potential of the population in case of an environmental change. In panmictic populations, disadvantageous mutants can persist around a steady state, determined by the rate of mutant production and the selective disadvantage (selection-mutation balance). In a fragmented system that consists of demes connected by migration, a steady-state persistence of disadvantageous mutants is also observed, which, however, is fundamentally different from the mutation-selection balance and characterized by higher mutant levels. The increase in mutant frequencies above the selection-mutation balance can be maintained in small ( N < N c ) demes as long as the migration rate is sufficiently small. The migration rate above which the mutants approach the selection-mutation balance decays exponentially with N / N c . The observed increase in the mutant numbers is not explained by the change in the effective population size. Implications for evolutionary processes in diseases are discussed, where the pre-existence of disadvantageous drug-resistant mutant cells or pathogens drives the response of the disease to treatments.

HIV infection dynamics and viral rebound: Modeling results from humanized mice.

Despite years of combined antiretroviral therapy (cART), HIV persists in infected individuals. The virus also rebounds after the cessation of cART. The sources contributing to viral persistence and rebound are not fully understood. When viral rebound occurs, what affects the time to rebound and how to delay the rebound remain unclear. In this paper, we started with the data fitting of an HIV infection model to the viral load data in treated and untreated humanized myeloid-only mice (MoM) in which macrophages serve as the target of HIV infection. By fixing the parameter values for macrophages from the MoM fitting, we fit a mathematical model including the infection of two target cell populations to the viral load data from humanized bone marrow/liver/thymus (BLT) mice, in which both CD4+ T cells and macrophages are the target of HIV infection. Data fitting suggests that the viral load decay in BLT mice under treatment has three phases. The loss of infected CD4+ T cells and macrophages is a major contributor to the first two phases of viral decay, and the last phase may be due to the latent infection of CD4+ T cells. Numerical simulations using parameter estimates from the data fitting show that the pre-ART viral load and the latent reservoir size at treatment cessation can affect viral growth rate and predict the time to viral rebound. Model simulations further reveal that early and prolonged cART can delay the viral rebound after cessation of treatment, which may have implications in the search for functional control of HIV infection.