Surrogate modeling and control of medical digital twins.

The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.

Control of Medical Digital Twins with Artificial Neural Networks.

The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.

Control of Medical Digital Twins with Artificial Neural Networks.

The objective of personalized medicine is to tailor interventions to an individual patient's unique characteristics. A key technology for this purpose involves medical digital twins, computational models of human biology that can be personalized and dynamically updated to incorporate patient-specific data collected over time. Certain aspects of human biology, such as the immune system, are not easily captured with physics-based models, such as differential equations. Instead, they are often multi-scale, stochastic, and hybrid. This poses a challenge to existing model-based control and optimization approaches that cannot be readily applied to such models. Recent advances in automatic differentiation and neural-network control methods hold promise in addressing complex control problems. However, the application of these approaches to biomedical systems is still in its early stages. This work introduces dynamics-informed neural-network controllers as an alternative approach to control of medical digital twins. As a first use case for this method, the focus is on agent-based models, a versatile and increasingly common modeling platform in biomedicine. The effectiveness of the proposed neural-network control method is illustrated and benchmarked against other methods with two widely-used agent-based model types. The relevance of the method introduced here extends beyond medical digital twins to other complex dynamical systems.

Toward mechanistic medical digital twins: some use cases in immunology.

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific-and practical-medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

Forum on immune digital twins: a meeting report.

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

MaHPIC malaria systems biology data from Plasmodium cynomolgi sporozoite longitudinal infections in macaques.

Plasmodium cynomolgi causes zoonotic malarial infections in Southeast Asia and this parasite species is important as a model for Plasmodium vivax and Plasmodium ovale. Each of these species produces hypnozoites in the liver, which can cause relapsing infections in the blood. Here we present methods and data generated from iterative longitudinal systems biology infection experiments designed and performed by the Malaria Host-Pathogen Interaction Center (MaHPIC) to delve deeper into the biology, pathogenesis, and immune responses of P. cynomolgi in the Macaca mulatta host. Infections were initiated by sporozoite inoculation. Blood and bone marrow samples were collected at defined timepoints for biological and computational experiments and integrative analyses revolving around primary illness, relapse illness, and subsequent disease and immune response patterns. Parasitological, clinical, haematological, immune response, and -omic datasets (transcriptomics, proteomics, metabolomics, and lipidomics) including metadata and computational results have been deposited in public repositories. The scope and depth of these datasets are unprecedented in studies of malaria, and they are projected to be a F.A.I.R., reliable data resource for decades.

Plasmodium knowlesi Cytoadhesion Involves SICA Variant Proteins.

Plasmodium knowlesi poses a health threat throughout Southeast Asian communities and currently causes most cases of malaria in Malaysia. This zoonotic parasite species has been studied in Macaca mulatta (rhesus monkeys) as a model for severe malarial infections, chronicity, and antigenic variation. The phenomenon of Plasmodium antigenic variation was first recognized during rhesus monkey infections. Plasmodium-encoded variant proteins were first discovered in this species and found to be expressed at the surface of infected erythrocytes, and then named the Schizont-Infected Cell Agglutination (SICA) antigens. SICA expression was shown to be spleen dependent, as SICA expression is lost after P. knowlesi is passaged in splenectomized rhesus. Here we present data from longitudinal P. knowlesi infections in rhesus with the most comprehensive analysis to date of clinical parameters and infected red blood cell sequestration in the vasculature of tissues from 22 organs. Based on the histopathological analysis of 22 tissue types from 11 rhesus monkeys, we show a comparative distribution of parasitized erythrocytes and the degree of margination of the infected erythrocytes with the endothelium. Interestingly, there was a significantly higher burden of parasites in the gastrointestinal tissues, and extensive margination of the parasites along the endothelium, which may help explain gastrointestinal symptoms frequently reported by patients with P. knowlesi malarial infections. Moreover, this margination was not observed in splenectomized rhesus that were infected with parasites not expressing the SICA proteins. This work provides data that directly supports the view that a subpopulation of P. knowlesi parasites cytoadheres and sequesters, likely via SICA variant antigens acting as ligands. This process is akin to the cytoadhesive function of the related variant antigen proteins, namely Erythrocyte Membrane Protein-1, expressed by Plasmodium falciparum.

Computational Modeling of Macrophage Iron Sequestration during Host Defense against Aspergillus.

Iron is essential to the virulence of Aspergillus species, and restricting iron availability is a critical mechanism of antimicrobial host defense. Macrophages recruited to the site of infection are at the crux of this process, employing multiple intersecting mechanisms to orchestrate iron sequestration from pathogens. To gain an integrated understanding of how this is achieved in aspergillosis, we generated a transcriptomic time series of the response of human monocyte-derived macrophages to Aspergillus and used this and the available literature to construct a mechanistic computational model of iron handling of macrophages during this infection. We found an overwhelming macrophage response beginning 2 to 4 h after exposure to the fungus, which included upregulated transcription of iron import proteins transferrin receptor-1, divalent metal transporter-1, and ZIP family transporters, and downregulated transcription of the iron exporter ferroportin. The computational model, based on a discrete dynamical systems framework, consisted of 21 3-state nodes, and was validated with additional experimental data that were not used in model generation. The model accurately captures the steady state and the trajectories of most of the quantitatively measured nodes. In the experimental data, we surprisingly found that transferrin receptor-1 upregulation preceded the induction of inflammatory cytokines, a feature that deviated from model predictions. Model simulations suggested that direct induction of transferrin receptor-1 (TfR1) after fungal recognition, independent of the iron regulatory protein-labile iron pool (IRP-LIP) system, explains this finding. We anticipate that this model will contribute to a quantitative understanding of iron regulation as a fundamental host defense mechanism during aspergillosis. IMPORTANCE Invasive pulmonary aspergillosis is a major cause of death among immunosuppressed individuals despite the best available therapy. Depriving the pathogen of iron is an essential component of host defense in this infection, but the mechanisms by which the host achieves this are complex. To understand how recruited macrophages mediate iron deprivation during the infection, we developed and validated a mechanistic computational model that integrates the available information in the field. The insights provided by this approach can help in designing iron modulation therapies as anti-fungal treatments.

Clinical recovery of Macaca fascicularis infected with Plasmodium knowlesi.

BACKGROUND: Kra monkeys (Macaca fascicularis), a natural host of Plasmodium knowlesi, control parasitaemia caused by this parasite species and escape death without treatment. Knowledge of the disease progression and resilience in kra monkeys will aid the effective use of this species to study mechanisms of resilience to malaria. This longitudinal study aimed to define clinical, physiological and pathological changes in kra monkeys infected with P. knowlesi, which could explain their resilient phenotype. METHODS: Kra monkeys (n = 15, male, young adults) were infected intravenously with cryopreserved P. knowlesi sporozoites and the resulting parasitaemias were monitored daily. Complete blood counts, reticulocyte counts, blood chemistry and physiological telemetry data (n = 7) were acquired as described prior to infection to establish baseline values and then daily after inoculation for up to 50 days. Bone marrow aspirates, plasma samples, and 22 tissue samples were collected at specific time points to evaluate longitudinal clinical, physiological and pathological effects of P. knowlesi infections during acute and chronic infections. RESULTS: As expected, the kra monkeys controlled acute infections and remained with low-level, persistent parasitaemias without anti-malarial intervention. Unexpectedly, early in the infection, fevers developed, which ultimately returned to baseline, as well as mild to moderate thrombocytopenia, and moderate to severe anaemia. Mathematical modelling and the reticulocyte production index indicated that the anaemia was largely due to the removal of uninfected erythrocytes and not impaired production of erythrocytes. Mild tissue damage was observed, and tissue parasite load was associated with tissue damage even though parasite accumulation in the tissues was generally low. CONCLUSIONS: Kra monkeys experimentally infected with P. knowlesi sporozoites presented with multiple clinical signs of malaria that varied in severity among individuals. Overall, the animals shared common mechanisms of resilience characterized by controlling parasitaemia 3-5 days after patency, and controlling fever, coupled with physiological and bone marrow responses to compensate for anaemia. Together, these responses likely minimized tissue damage while supporting the establishment of chronic infections, which may be important for transmission in natural endemic settings. These results provide new foundational insights into malaria pathogenesis and resilience in kra monkeys, which may improve understanding of human infections.

Dramatic transcriptomic differences in Macaca mulatta and Macaca fascicularis with Plasmodium knowlesi infections.

Plasmodium knowlesi, a model malaria parasite, is responsible for a significant portion of zoonotic malaria cases in Southeast Asia and must be controlled to avoid disease severity and fatalities. However, little is known about the host-parasite interactions and molecular mechanisms in play during the course of P. knowlesi malaria infections, which also may be relevant across Plasmodium species. Here we contrast P. knowlesi sporozoite-initiated infections in Macaca mulatta and Macaca fascicularis using whole blood RNA-sequencing and transcriptomic analysis. These macaque hosts are evolutionarily close, yet malaria-naïve M. mulatta will succumb to blood-stage infection without treatment, whereas malaria-naïve M. fascicularis controls parasitemia without treatment. This comparative analysis reveals transcriptomic differences as early as the liver phase of infection, in the form of signaling pathways that are activated in M. fascicularis, but not M. mulatta. Additionally, while most immune responses are initially similar during the acute stage of the blood infection, significant differences arise subsequently. The observed differences point to prolonged inflammation and anti-inflammatory effects of IL10 in M. mulatta, while M. fascicularis undergoes a transcriptional makeover towards cell proliferation, consistent with its recovery. Together, these findings suggest that timely detection of P. knowlesi in M. fascicularis, coupled with control of inflammation while initiating the replenishment of key cell populations, helps contain the infection. Overall, this study points to specific genes and pathways that could be investigated as a basis for new drug targets that support recovery from acute malaria.

A model of dopamine and serotonin-kynurenine metabolism in cortisolemia: Implications for depression.

A major factor contributing to the etiology of depression is a neurochemical imbalance of the dopaminergic and serotonergic systems, which is caused by persistently high levels of circulating stress hormones. Here, a computational model is proposed to investigate the interplay between dopaminergic and serotonergic-kynurenine metabolism under cortisolemia and its consequences for the onset of depression. The model was formulated as a set of nonlinear ordinary differential equations represented with power-law functions. Parameter values were obtained from experimental data reported in the literature, biological databases, and other general information, and subsequently fine-tuned through optimization. Model simulations predict that changes in the kynurenine pathway, caused by elevated levels of cortisol, can increase the risk of neurotoxicity and lead to increased levels of 3,4-dihydroxyphenylaceltahyde (DOPAL) and 5-hydroxyindoleacetaldehyde (5-HIAL). These aldehydes contribute to alpha-synuclein aggregation and may cause mitochondrial fragmentation. Further model analysis demonstrated that the inhibition of both serotonin transport and kynurenine-3-monooxygenase decreased the levels of DOPAL and 5-HIAL and the neurotoxic risk often associated with depression. The mathematical model was also able to predict a novel role of the dopamine and serotonin metabolites DOPAL and 5-HIAL in the ethiology of depression, which is facilitated through increased cortisol levels. Finally, the model analysis suggests treatment with a combination of inhibitors of serotonin transport and kynurenine-3-monooxygenase as a potentially effective pharmacological strategy to revert the slow-down in monoamine neurotransmission that is often triggered by inflammation.

Thickness of the airway surface liquid layer in the lung is affected in cystic fibrosis by compromised synergistic regulation of the ENaC ion channel.

The lung epithelium is lined with a layer of airway surface liquid (ASL) that is crucial for healthy lung function. ASL thickness is controlled by two ion channels: epithelium sodium channel (ENaC) and cystic fibrosis (CF) transmembrane conductance regulator (CFTR). Here, we present a minimal mathematical model of ENaC, CFTR and ASL regulation that sheds light on the control of ENaC by the short palate lung and nasal epithelial clone 1 (SPLUNC1) protein and by phosphatidylinositol 4,5-biphosphate (PI(4,5)P2). The model, despite its simplicity, yields a good fit to experimental observations and is an effective tool for exploring the interplay between ENaC, CFTR and ASL. Steady-state data and dynamic information constrain the model's parameters without ambiguities. Testing the hypothesis that PI(4,5)P2 protects ENaC from ubiquitination suggests that this protection does not improve the model results and that the control of the ENaC opening probability by PI(4,5)P2 is sufficient to explain all available data. The model analysis further demonstrates that ASL at the steady state is sensitive to small changes in PI(4,5)P2 abundance, particularly in CF conditions, which suggests that manipulation of phosphoinositide metabolism may promote therapeutic benefits for CF patients.

Analysis of erythrocyte dynamics in Rhesus macaque monkeys during infection with Plasmodium cynomolgi.

BACKGROUND: Malaria is a major mosquito transmitted, blood-borne parasitic disease that afflicts humans. The disease causes anaemia and other clinical complications, which can lead to death. Plasmodium vivax is known for its reticulocyte host cell specificity, but many gaps in disease details remain. Much less is known about the closely related species, Plasmodium cynomolgi, although it is naturally acquired and causes zoonotic malaria. Here, a computational model is developed based on longitudinal analyses of P. cynomolgi infections in nonhuman primates to investigate the erythrocyte dynamics that is pertinent to understanding both P. cynomolgi and P. vivax malaria in humans. METHODS: A cohort of five P. cynomolgi infected Rhesus macaques (Macaca mulatta) is studied, with individuals exhibiting a plethora of clinical outcomes, including varying levels of anaemia. A discrete recursive model with age structure is developed to replicate the dynamics of P. cynomolgi blood-stage infections. The model allows for parasitic reticulocyte preference and assumes an age preference among the mature RBCs. RBC senescence is modelled using a hazard function, according to which RBCs have a mean lifespan of 98 ± 21 days. RESULTS: Based on in vivo data from three cohorts of macaques, the computational model is used to characterize the reticulocyte lifespan in circulation as 24 ± 5 h (n = 15) and the rate of RBC production as 2727 ± 209 cells/h/µL (n = 15). Analysis of the host responses reveals a pre-patency increase in the number of reticulocytes. It also allows the quantification of RBC removal through the bystander effect. CONCLUSIONS: The evident pre-patency increase in reticulocytes is due to a shift towards the release of younger reticulocytes, which could result from a parasite-induced factor meant to increase reticulocyte availability and satisfy the parasite's tropism, which has an average value of 32:1 in this cohort. The number of RBCs lost due to the bystander effect relative to infection-induced RBC losses is 62% for P. cynomolgi infections, which is substantially lower than the value of 95% previously determined for another simian species, Plasmodium coatneyi.

A dynamic model of lignin biosynthesis in Brachypodium distachyon.

BACKGROUND: Lignin is a crucial molecule for terrestrial plants, as it offers structural support and permits the transport of water over long distances. The hardness of lignin reduces plant digestibility by cattle and sheep; it also makes inedible plant materials recalcitrant toward the enzymatic fermentation of cellulose, which is a potentially valuable substrate for sustainable biofuels. Targeted attempts to change the amount or composition of lignin in relevant plant species have been hampered by the fact that the lignin biosynthetic pathway is difficult to understand, because it uses several enzymes for the same substrates, is regulated in an ill-characterized manner, may operate in different locations within cells, and contains metabolic channels, which the plant may use to funnel initial substrates into specific monolignols. RESULTS: We propose a dynamic mathematical model that integrates various datasets and other information regarding the lignin pathway in Brachypodium distachyon and permits explanations for some counterintuitive observations. The model predicts the lignin composition and label distribution in a BdPTAL knockdown strain, with results that are quite similar to experimental data. CONCLUSION: Given the present scarcity of available data, the model resulting from our analysis is presumably not final. However, it offers proof of concept for how one may design integrative pathway models of this type, which are necessary tools for predicting the consequences of genomic or other alterations toward plants with lignin features that are more desirable than in their wild-type counterparts.

A Mathematical Model of the Phosphoinositide Pathway.

Phosphoinositides are signalling lipids that constitute a complex network regulating many cellular processes. We propose a computational model that accounts for all species of phosphoinositides in the plasma membrane of mammalian cells. The model replicates the steady-state of the pathway and most known dynamic phenomena. Sensitivity analysis demonstrates model robustness to alterations in the parameters. Model analysis suggest that the greatest contributor to phosphatidylinositol 4,5-biphosphate (PI(4,5)P2) production is a flux representing the direct transformation of PI into PI(4,5)P2, also responsible for the maintenance of this pool when phosphatidylinositol 4-phosphate (PI(4)P) is decreased. PI(5)P is also shown to be a significant source for PI(4,5)P2 production. The model was validated with siRNA screens that knocked down the expression of enzymes in the pathway. The screen monitored the activity of the epithelium sodium channel (ENaC), which is activated by PI(4,5)P2. While the model may deepen our understanding of other physiological processes involving phosphoinositides, we highlight therapeutic effects of ENaC modulation in Cystic Fibrosis (CF). The model suggests control strategies where the activities of the enzyme phosphoinositide 4-phosphate 5-kinase I (PIP5KI) or the PI4K + PIP5KI + DVL protein complex are decreased and cause an efficacious reduction in PI(4,5)P2 levels while avoiding undesirable alterations in other phosphoinositide pools.

Mathematical models of lignin biosynthesis.

BACKGROUND: Lignin is a natural polymer that is interwoven with cellulose and hemicellulose within plant cell walls. Due to this molecular arrangement, lignin is a major contributor to the recalcitrance of plant materials with respect to the extraction of sugars and their fermentation into ethanol, butanol, and other potential bioenergy crops. The lignin biosynthetic pathway is similar, but not identical in different plant species. It is in each case comprised of a moderate number of enzymatic steps, but its responses to manipulations, such as gene knock-downs, are complicated by the fact that several of the key enzymes are involved in several reaction steps. This feature poses a challenge to bioenergy production, as it renders it difficult to select the most promising combinations of genetic manipulations for the optimization of lignin composition and amount. RESULTS: Here, we present several computational models than can aid in the analysis of data characterizing lignin biosynthesis. While minimizing technical details, we focus on the questions of what types of data are particularly useful for modeling and what genuine benefits the biofuel researcher may gain from the resulting models. We demonstrate our analysis with mathematical models for black cottonwood (Populus trichocarpa), alfalfa (Medicago truncatula), switchgrass (Panicum virgatum) and the grass Brachypodium distachyon. CONCLUSIONS: Despite commonality in pathway structure, different plant species show different regulatory features and distinct spatial and topological characteristics. The putative lignin biosynthes pathway is not able to explain the plant specific laboratory data, and the necessity of plant specific modeling should be heeded.

Metabolic modeling helps interpret transcriptomic changes during malaria.

Disease represents a specific case of malfunctioning within a complex system. Whereas it is often feasible to observe and possibly treat the symptoms of a disease, it is much more challenging to identify and characterize its molecular root causes. Even in infectious diseases that are caused by a known parasite, it is often impossible to pinpoint exactly which molecular profiles of components or processes are directly or indirectly altered. However, a deep understanding of such profiles is a prerequisite for rational, efficacious treatments. Modern omics methodologies are permitting large-scale scans of some molecular profiles, but these scans often yield results that are not intuitive and difficult to interpret. For instance, the comparison of healthy and diseased transcriptome profiles may point to certain sets of involved genes, but a host of post-transcriptional processes and regulatory mechanisms renders predictions regarding metabolic or physiological consequences of the observed changes in gene expression unreliable. Here we present proof of concept that dynamic models of metabolic pathway systems may offer a tool for interpreting transcriptomic profiles measured during disease. We illustrate this strategy with the interpretation of expression data of genes coding for enzymes associated with purine metabolism. These data were obtained during infections of rhesus macaques (Macaca mulatta) with the malaria parasite Plasmodium cynomolgi or P. coatneyi. The model-based interpretation reveals clear patterns of flux redistribution within the purine pathway that are consistent between the two malaria pathogens and are even reflected in data from humans infected with P. falciparum. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

The three-legged stool of understanding metabolism: integrating metabolomics with biochemical genetics and computational modeling.

Traditional biochemical research has resulted in a good understanding of many aspects of metabolism. However, this reductionist approach is time consuming and requires substantial resources, thus raising the question whether modern metabolomics and genomics should take over and replace the targeted experiments of old. We proffer that such a replacement is neither feasible not desirable and propose instead the tight integration of modern, system-wide omics with traditional experimental bench science and dedicated computational approaches. This integration is an important prerequisite toward the optimal acquisition of knowledge regarding metabolism and physiology in health and disease. The commentary describes advantages and drawbacks of current approaches to assessing metabolism and highlights the challenges to be overcome as we strive to achieve a deeper level of metabolic understanding in the future.

A model of Plasmodium vivax concealment based on Plasmodium cynomolgi infections in Macaca mulatta.

BACKGROUND: Plasmodium vivax can cause severe malaria. The total parasite biomass during infections is correlated with the severity of disease but not necessarily quantified accurately by microscopy. This finding has raised the question whether there could be sub-populations of parasites that are not observed in peripheral blood smears but continue to contribute to the increase in parasite numbers that drive pathogenesis. Non-human primate infection models utilizing the closely related simian malaria parasite Plasmodium cynomolgi hold the potential for quantifying the magnitude of possibly unobserved infected red blood cell (iRBC) populations and determining how the presence of this hidden reservoir correlates with disease severity. METHODS: Time series data tracking the longitudinal development of parasitaemia in five Macaca mulatta infected with P. cynomolgi were used to design a computational model quantifying iRBCs that circulate in the blood versus those that are not detectable and are termed here as 'concealed'. This terminology is proposed to distinguish such observations from the deep vascular and widespread 'sequestration' of Plasmodium falciparum iRBCs, which is governed by distinctly different molecular mechanisms. RESULTS: The computational model presented here clearly demonstrates that the observed growth data of iRBC populations are not consistent with the known biology and blood-stage cycle of P. cynomolgi. However, the discrepancies can be resolved when a sub-population of concealed iRBCs is taken into account. The model suggests that the early growth of a hidden parasite sub-population has the potential to drive disease. As an alternative, the data could be explained by the sequential release of merozoites from the liver over a number of days, but this scenario seems less likely. CONCLUSIONS: Concealment of a non-circulating iRBC sub-population during P. cynomolgi infection of M. mulatta is an important aspect of this successful host-pathogen relationship. The data also support the likelihood that a sub-population of iRBCs of P. vivax has a comparable means to become withdrawn from the peripheral circulation. This inference has implications for understanding vivax biology and pathogenesis and stresses the importance of considering a concealed parasite reservoir with regard to vivax epidemiology and the quantification and treatment of P. vivax infections.

Quantifying the removal of red blood cells in Macaca mulatta during a Plasmodium coatneyi infection.

BACKGROUND: Malaria is the most deadly parasitic disease in humans globally, and the long-time coexistence with malaria has left indelible marks in the human genome that are the causes of a variety of genetic disorders. Although anaemia is a common clinical complication of malaria, the root causes and mechanisms involved in the pathogenesis of malarial anaemia are unclear and difficult to study in humans. Non-human primate (NHP) model systems enable the mechanistic study and quantification of underlying causative factors of malarial anaemia, and particularly the onset of severe anaemia. METHODS: Data were obtained in the course of Plasmodium coatneyi infections of malaria-naïve and semi-immune rhesus macaques (Macaca mulatta), whose red blood cells (RBCs) were labelled in situ with biotin at the time the infections were initiated. The data were used for a survival analysis that permitted, for the first time, an accurate estimation of the lifespan of erythrocytes in macaques. The data furthermore formed the basis for the development and parameterization of a recursive dynamic model of erythrocyte turnover, which was used for the quantification of RBC production and removal in each macaque. RESULTS: The computational analysis demonstrated that the lifespan of erythrocytes in macaques is 98 ± 21 days. The model also unambiguously showed that death due to senescence and parasitaemia is not sufficient to account for the extent of infection-induced anaemia. Specifically, the model permits, for the first time, the quantification of the different causes of RBC death, namely, normal senescence, age-independent random loss, parasitization, and bystander effects in uninfected cells. Such a dissection of the overall RBC removal process is hardly possible with experimental means alone. In the infected malaria-naïve macaques, death of erythrocytes by normal physiological senescence processes accounts for 20 % and parasitization for only 4 %, whereas bystander effects are associated with an astonishing 76 % of total RBC losses. Model-based comparisons of alternative mechanisms involved in the bystander effect revealed that most of the losses are likely due to a process of removing uninfected RBCs of all age classes and only minimally due to an increased rate of senescence of the uninfected RBCs. CONCLUSIONS: A new malaria blood-stage model was developed for the analysis of data characterizing P. coatneyi infections of M. mulatta. The model used a discrete and recursive framework with age-structure that allowed the quantification of the most significant pathophysiological processes of RBC removal. The computational results revealed that the malarial anaemia caused by this parasite is mostly due to a loss of uninfected RBCs by an age-independent process. The biological identity and complete mechanism of this process is not fully understood and requires further investigation.