Mutation, fitness, viral diversity, and predictive markers of disease progression in a computational model of HIV type 1 infection

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling

Background

Immune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.

Aim

Studies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease.

Methods

We use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics.

Results

By assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

Computer Modeling of Clonal Dominance: Memory-Anti-Naïve and Its Curbing by Attrition

Experimental and computational studies have revealed that T-cell cross-reactivity is a widespread phenomenon that can either be advantageous or detrimental to the host. In particular, detrimental effects can occur whenever the clonal dominance of memory cells is not justified by their infection-clearing capacity. Using an agent-based model of the immune system, we recently predicted the "memory anti-naïve" phenomenon, which occurs when the secondary challenge is similar but not identical to the primary stimulation. In this case, the pre-existing memory cells formed during the primary infection may be rapidly deployed in spite of their low affinity and can actually prevent a potentially higher affinity naïve response from emerging, resulting in impaired viral clearance. This finding allowed us to propose a mechanistic explanation for the concept of "antigenic sin" originally described in the context of the humoral response. However, the fact that antigenic sin is a relatively rare occurrence suggests the existence of evolutionary mechanisms that can mitigate the effect of the memory anti-naïve phenomenon. In this study we use computer modeling to further elucidate clonal dominance and the memory anti-naïve phenomenon, and to investigate a possible mitigating factor called attrition. Attrition has been described in the experimental and computational literature as a combination of competition for space and apoptosis of lymphocytes via type-I interferon in the early stages of a viral infection. This study systematically explores the relationship between clonal dominance and the mechanism of attrition. Our results suggest that attrition can indeed mitigate the memory anti-naïve effect by enabling the emergence of a diverse, higher affinity naïve response against the secondary challenge. In conclusion, modeling attrition allows us to shed light on the nature of clonal interaction and dominance.

A study on the dynamics of temporary HIV treatment to assess the controversial outcomes of clinical trials: An in-silico approach

It is still unclear under which conditions temporary combined antiretroviral therapy (cART) results in a prolonged remission after interruption. Clinical trials have contradicting reposts about the effect of cART during primary HIV infection on the disease progression. Here we propose that the apparent contradiction is due the presence of a window of opportunity for cART treatment observed in the in silico studies. We study non-linear correlations in the HIV dynamics over time using information theory. This approach requires a large dataset of CD4+ T lymphocytes and viral load concentrations over time. Since it is unfeasible to collect the required amount of data in clinical trials we use C-ImmSim, a clinically validated in silico model of the HIV infection, to simulate the HIV infection and temporary cART in 500 virtual patients for a period of 6 years post infection in time steps of 8 hours. We validate the results of our model with two published clinical trials of temporary cART in acute infection and analyse the impact of cART on the immune response. Our quantitative analysis predicts a “window of opportunity” of about ten months after the acute phase during which a temporary cART has significantly longer-lasting beneficial effects on the immune system as compared to treatment during the chronic phase. This window may help to explain the controversial outcomes of clinical trials that differ by the starting time and duration of the short-term course cART and provides a critical insight to develop appropriate protocols for future clinical trials.

HIV-1-infected T-cells dynamics and prognosis: An evolutionary game model

BACKGROUND AND OBJECTIVE

Understanding the dynamics of human immunodeficiency virus (HIV) is essential for depicting, developing, and investigating effective treatment strategies. HIV infects several types of immune cells, but its main target is to destroy helper T-cells. In the lymph nodes, the infected T-cells interact with each other and their environment to obtain more resources. According to infectivity and replicative capacity of T-cells in the HIV infection process, they can be divided into four phenotypes. Although genetic mutations in the reverse transcription that beget these phenotypes are random, the framework by which a phenotype become favored is affected by the environment and neighboring phenotypes. Moreover, the HIV disease has all components of an evolutionary process, including replication, mutation, and selection.

METHODS

We propose a novel structure-based game-theoretic model for the evolution of HIV-1-Infected CD4+T-cells and invasion of the immune system. We discuss the theoretical basis of the stable equilibrium states of the evolutionary dynamics of four T-cells types as well as its significant results to understand and control HIV infection. The results include the importance of genetic variations and the process of establishing evolutionary dynamics of the virus quasispecies.

RESULTS

Our results show that there is a direct dependency between some parameters such as mutation rates and the stability of equilibrium states in the HIV infection. This is an interesting result because these parameters can be changed by some pharmacotherapies and alternative treatments. Our model indicates that in an appropriate treatment the relative frequency of the wild type of virus quasispecies can be decreased in the population. Consequently, this can cause delaying the emergence of the AIDS phase. To assess the model, we investigate two new treatments for HIV. The results show that our model can predict the treatment results.

CONCLUSIONS

The paper shows that a structured-based evolutionary game theory can model the evolutionary dynamics of the infected T-cells and virus quasispecies. The model predicts certain aspects of the HIV infection process under several treatments.

The Applications of Genetic Algorithms in Medicine

A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].

How the interval between prime and boost injection affects the immune response in a computational model of the immune system

The immune system is able to respond more vigorously to the second contact with a given antigen than to the first contact. Vaccination protocols generally include at least two doses, in order to obtain high antibody titers. We want to analyze the relation between the time elapsed from the first dose (priming) and the second dose (boost) on the antibody titers. In this paper, we couple in vivo experiments with computer simulations to assess the effect of delaying the second injection. We observe that an interval of several weeks between the prime and the boost is necessary to obtain optimal antibody responses.

HIV reservoirs and immune surveillance evasion cause the failure of structured treatment interruptions: a computational study

Continuous antiretroviral therapy is currently the most effective way to treat HIV infection. Unstructured interruptions are quite common due to side effects and toxicity, among others, and cannot be prevented. Several attempts to structure these interruptions failed due to an increased morbidity compared to continuous treatment. The cause of this failure is poorly understood and often attributed to drug resistance. Here we show that structured treatment interruptions would fail regardless of the emergence of drug resistance. Our computational model of the HIV infection dynamics in lymphoid tissue inside lymph nodes, demonstrates that HIV reservoirs and evasion from immune surveillance themselves are sufficient to cause the failure of structured interruptions. We validate our model with data from a clinical trial and show that it is possible to optimize the schedule of interruptions to perform as well as the continuous treatment in the absence of drug resistance. Our methodology enables studying the problem of treatment optimization without having impact on human beings. We anticipate that it is feasible to steer new clinical trials using computational models.

Within-host demographic fluctuations and correlations in early retroviral infection

In this paper we analyze the demographic fluctuations and correlations present in within-host populations of viruses and their target cells during the early stages of infection. In particular, we present an exact treatment of a discrete-population, stochastic, continuous-time master equation description of HIV or similar retroviral infection dynamics, employing Monte Carlo simulations. The results of calculations employing Gillespie's direct method clearly demonstrate the importance of considering the microscopic details of the interactions which constitute the macroscopic dynamics. We then employ the τ-leaping approach to study the statistical characteristics of infections involving realistic absolute numbers of within-host viral and cellular populations, before going on to investigate the effect that initial viral population size plays on these characteristics. Our main conclusion is that cross-correlations between infected cell and virion populations alter dramatically over the course of the infection. We suggest that these statistical correlations offer a novel and robust signature for the acute phase of retroviral infection.

Timely HAART initiation may pave the way for a better viral control

BACKGROUND

When to initiate antiretroviral therapy in HIV infected patients is a difficult clinical decision. Actually, it is still a matter of discussion whether early highly active antiretroviral therapy (HAART) during primary HIV infection may influence the dynamics of the viral rebound, in case of therapy interruption, and overall the main disease course.

METHODS

In this article we use a computational model and clinical data to identify the role of HAART timing on the residual capability to control HIV rebound after treatment suspension. Analyses of clinical data from three groups of patients initiating HAART respectively before seroconversion (very early), during the acute phase (early) and in the chronic phase (late), evidence differences arising from the very early events of the viral infection.

RESULTS

The computational model allows a fine grain assessment of the impact of HAART timing on the disease outcome, from acute to chronic HIV-1 infection. Both patients' data and computer simulations reveal that HAART timing may indeed affect the HIV control capability after treatment discontinuation. In particular, we find a median time to viral rebound that is significantly longer in very early than in late patients.

CONCLUSIONS

A timing threshold is identified, corresponding to approximately three weeks post-infection, after which the capability to control HIV replication is lost. Conversely, HAART initiation occurring within three weeks from the infection could allow to preserve a significant control capability. This time could be related to the global triggering of uncontrolled immune activation, affecting residual immune competence preservation and HIV reservoir establishment.

Criticality of timing for anti-HIV therapy initiation

The time of initiation of antiretroviral therapy in HIV-1 infected patients has a determinant effect on the viral dynamics. The question is, how far can the therapy be delayed? Is sooner always better? We resort to clinical data and to microsimulations to forecast the dynamics of the viral load at therapy interruption after prolonged antiretroviral treatment. A computational model previously evaluated, produces results that are statistically adherent to clinical data. In addition, it allows a finer grain analysis of the impact of the therapy initiation point to the disease course. We find a swift increase of the viral density as a function of the time of initiation of the therapy measured when the therapy is stopped. In particular there is a critical time delay with respect to the infection instant beyond which the therapy does not affect the viral rebound. Initiation of the treatment is beneficial because it can down-regulate the immune activation, hence limiting viral replication and spread.

Model refinement through high-performance computing: an agent-based HIV example

BACKGROUND

Recent advances in Immunology highlighted the importance of local properties on the overall progression of HIV infection. In particular, the gastrointestinal tract is seen as a key area during early infection, and the massive cell depletion associated with it may influence subsequent disease progression. This motivated the development of a large-scale agent-based model.

RESULTS

Lymph nodes are explicitly implemented, and considerations on parallel computing permit large simulations and the inclusion of local features. The results obtained show that GI tract inclusion in the model leads to an accelerated disease progression, during both the early stages and the long-term evolution, compared to a theoretical, uniform model.

CONCLUSIONS

These results confirm the potential of treatment policies currently under investigation, which focus on this region. They also highlight the potential of this modelling framework, incorporating both agent-based and network-based components, in the context of complex systems where scaling-up alone does not result in models providing additional insights.

Human immunodeficiency virus type-1 group M quasispecies evolution: diversity and divergence in patients co-infected with active tuberculosis

The evolution of intra-host human immunodeficiency virus type 1 (HIV-1) quasispecies prior and after treating active tuberculosis (TB) with chemotherapy in HIV-1/TB patients was assessed. Two time points HIV-1 quasispecies were evaluated by comparing HIV-1-infected patients with active tuberculosis (HIV-1/TB) and HIV-1-infected patients without tuberculosis (HIV-1/non-TB). Plasma samples were obtained from the Frankfurt HIV cohort, and HIV-1 RNA was isolated. C2V5 env was amplified by PCR and molecular cloning was performed. Eight to twenty-five clones were sequenced from each patient. Various phylogenetic analyses were performed. We found a significant increase in diversity and divergence in HIV-1/TB compared to the HIV-1/non-TB. For HIV-1/TB, the average rate of evolution of C2V5 env was higher than previous reports (2.4 × 10(-4) substitution/site/day). Two groups of HIV-1/TB were observed based on the rate of HIV-1 evolution and coreceptor usage: A fast evolving R5-tropic dominating group and a relatively slowly evolving X4 group. The results demonstrated that active TB has an impact on HIV-1 viral diversity and divergence over time. The influence of active TB on longitudinal evolution of HIV-1 may be predominant for R5 viruses.

ImmunoGrid: towards agent-based simulations of the human immune system at a natural scale

The ultimate aim of the EU-funded ImmunoGrid project is to develop a natural-scale model of the human immune system-that is, one that reflects both the diversity and the relative proportions of the molecules and cells that comprise it-together with the grid infrastructure necessary to apply this model to specific applications in the field of immunology. These objectives present the ImmunoGrid Consortium with formidable challenges in terms of complexity of the immune system, our partial understanding about how the immune system works, the lack of reliable data and the scale of computational resources required. In this paper, we explain the key challenges and the approaches adopted to overcome them. We also consider wider implications for the present ambitious plans to develop natural-scale, integrated models of the human body that can make contributions to personalized health care, such as the European Virtual Physiological Human initiative. Finally, we ask a key question: How long will it take us to resolve these challenges and when can we expect to have fully functional models that will deliver health-care benefits in the form of personalized care solutions and improved disease prevention?

Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system

We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein-protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.

Immune control of HIV-1 infection after therapy interruption: immediate versus deferred antiretroviral therapy

BACKGROUND

The optimal stage for initiating antiretroviral therapies in HIV-1 bearing patients is still a matter of debate.

METHODS

We present computer simulations of HIV-1 infection aimed at identifying the pro et contra of immediate as compared to deferred Highly Active Antiretroviral Therapy (HAART).

RESULTS

Our simulations highlight that a prompt specific CD8+ cytotoxic T lymphocytes response is detected when therapy is delayed. Compared to very early initiation of HAART, in deferred treated patients CD8+ T cells manage to mediate the decline of viremia in a shorter time and, at interruption of therapy, the virus experiences a stronger immune pressure. We also observe, however, that the immunological effects of the therapy fade with time in both therapeutic regimens. Thus, within one year from discontinuation, viral burden recovers to the value at which it would level off in the absence of therapy.In summary, simulations show that immediate therapy does not prolong the disease-free period and does not confer a survival benefit when compared to treatment started during the chronic infection phase.

CONCLUSION

Our conclusion is that, since there is no therapy to date that guarantees life-long protection, deferral of therapy should be preferred in order to minimize the risk of adverse effects, the occurrence of drug resistances and the costs of treatment.

Stochastic modelling of genotypic drug-resistance for human immunodeficiency virus towards long-term combination therapy optimization

MOTIVATION

Several mathematical models have been investigated for the description of viral dynamics in the human body: HIV-1 infection is a particular and interesting scenario, because the virus attacks cells of the immune system that have a role in the antibody production and its high mutation rate permits to escape both the immune response and, in some cases, the drug pressure. The viral genetic evolution is intrinsically a stochastic process, eventually driven by the drug pressure, dependent on the drug combinations and concentration: in this article the viral genotypic drug resistance onset is the main focus addressed. The theoretical basis is the modelling of HIV-1 population dynamics as a predator-prey system of differential equations with a time-dependent therapy efficacy term, while the viral genome mutation evolution follows a Poisson distribution. The instant probabilities of drug resistance are estimated by means of functions trained from in vitro phenotypes, with a roulette-wheel-based mechanisms of resistant selection. Simulations have been designed for treatments made of one and two drugs as well as for combination antiretroviral therapies. The effect of limited adherence to therapy was also analyzed. Sequential treatment change episodes were also exploited with the aim to evaluate optimal synoptic treatment scenarios.

RESULTS

The stochastic predator-prey modelling usefully predicted long-term virologic outcomes of evolved HIV-1 strains for selected antiretroviral therapy combinations. For a set of widely used combination therapies, results were consistent with findings reported in literature and with estimates coming from analysis on a large retrospective data base (EuResist).

The role of the humoral immune response in the molecular evolution of the envelope C2, V3 and C3 regions in chronically HIV-2 infected patients

Background

This study was designed to investigate, for the first time, the short-term molecular evolution of the HIV-2 C2, V3 and C3 envelope regions and its association with the immune response. Clonal sequences of the env C2V3C3 region were obtained from a cohort of eighteen HIV-2 chronically infected patients followed prospectively during 2–4 years. Genetic diversity, divergence, positive selection and glycosylation in the C2V3C3 region were analysed as a function of the number of CD4+ T cells and the anti-C2V3C3 IgG and IgA antibody reactivity

Results

The mean intra-host nucleotide diversity was 2.1% (SD, 1.1%), increasing along the course of infection in most patients. Diversity at the amino acid level was significantly lower for the V3 region and higher for the C2 region. The average divergence rate was 0.014 substitutions/site/year, which is similar to that reported in chronic HIV-1 infection. The number and position of positively selected sites was highly variable, except for codons 267 and 270 in C2 that were under strong and persistent positive selection in most patients. N-glycosylation sites located in C2 and V3 were conserved in all patients along the course of infection. Intra-host variation of C2V3C3-specific IgG response over time was inversely associated with the variation in nucleotide and amino acid diversity of the C2V3C3 region. Variation of the C2V3C3-specific IgA response was inversely associated with variation in the number of N-glycosylation sites.

Conclusion

The evolutionary dynamics of HIV-2 envelope during chronic aviremic infection is similar to HIV-1 implying that the virus should be actively replicating in cellular compartments. Convergent evolution of N-glycosylation in C2 and V3, and the limited diversification of V3, indicates that there are important functional constraints to the potential diversity of the HIV-2 envelope. C2V3C3-specific IgG antibodies are effective at reducing viral population size limiting the number of virus escape mutants. The C3 region seems to be a target for IgA antibodies and increasing N-linked glycosylation may prevent HIV-2 envelope recognition by these antibodies. Our results provide new insights into the biology of HIV-2 and its relation with the human host and may have important implications for vaccine design.

An enhanced agent based model of the immune system response

We describe some recent enhancements introduced in C-ImmSim, a simulator of the immune system response that we have been developing for a number of years along with preliminary results produced by the simulation of the Highly Active Anti-Retroviral Therapy in HIV-1 infected patients.

Gene expression profiling of gut mucosa and mesenteric lymph nodes in simian immunodeficiency virus-infected macaques with divergent disease course

BACKGROUND

Although the majority of drug-naïve HIV-infected patients develop acquired immunodeficiency syndrome (AIDS), a small percentage remains asymptomatic without therapeutic intervention.

METHODS

We have utilized the simian immunodeficiency virus (SIV)-infected rhesus macaque model to gain insights into the molecular mechanisms of long-term protection against simian AIDS.

RESULTS

Chronically SIV-infected macaques with disease progression had high viral loads and CD4(+) T-cell depletion in mucosal tissue and peripheral blood. These animals displayed pathologic changes in gut-associated lymphoid tissue (GALT) and mesenteric lymph node that coincided with increased expression of genes associated with interferon induction, inflammation and immune activation. In contrast, the animal with long-term asymptomatic infection suppressed viral replication and maintained CD4(+) T cells in both GALT and peripheral blood while decreasing expression of genes involved in inflammation and immune activation.

CONCLUSIONS

Our findings suggest that reduced immune activation and effective repair and regeneration of mucosal tissues correlate with long-term survival in SIV-infected macaques.

Role of avidity and breadth of the CD4 T cell response in progression to AIDS

The great variability in the time between infection with HIV and the onset of AIDS has been the object of intense study. In the current work, we examine a mathematical model that focuses on the role of immune response variability between patients. We study the effect of variation in both the avidity and the breadth of the immune response on within-patient disease dynamics, viral setpoint and time to AIDS. We conclude that immune response variability can explain the observed variability in disease progression to a large extent. It turns out that the avidity, more than the breadth of the immune response, determines disease progression, and that the average avidity of the five best clones is a much better correlate for disease progression than the total number of clones responding. For the design of vaccines, this would suggest that, if given the choice between stimulating a broader, but average avidity response or a narrower high-avidity response, the latter option would yield better control of virus load and consequently slow down disease progression.

The role of computational models of the immune system in designing vaccination strategies

Mathematical and computational models are designed to improve our understanding of biological phenomena, to confirm/reject hypotheses, and to find points of intervention by altering the behavior of the studied systems. Here we describe the role of mathematical/computational models of the immune system. In particular, we analyze some examples of how mathematical modeling can contribute to finding optimal vaccination strategies. Indeed, computational modeling offers an intriguing opportunity from the theoretical point of view, and it will be of interest for clinically oriented investigators who wish to find optimal therapeutic strategies and for pharmaceutical industries that want to produce effective and successful drugs.