SimB16: Modeling Induced Immune System Response against B16-Melanoma

Immunological therapy of progressive tumors requires not only activation and expansion of tumor specific cytotoxic T lymphocytes (CTLs), but also an efficient effector phase including migration of CTLs in the tumor tissue followed by conjugation and killing of target cells. We report the application of an agent-based model to recapitulate both the effect of a specific immunotherapy strategy against B16-melanoma in mice and the tumor progression in a generic tissue section. A comparison of the in silico results with the in vivo experiments shows excellent agreement. We therefore use the model to predict a critical role for CD137 expression on tumor vessel endothelium for successful therapy and other mechanistic aspects. Experimental results are fully compatible with the model predictions. The biologically oriented in silico model derived in this work will be used to predict treatment failure or success in other pre-clinical conditions eventually leading new promising in vivo experiments

Modeling the competition between lung metastases and the immune system using agents

<p>Abstract</p> <p>Background</p> <p>The Triplex cell vaccine is a cancer cellular vaccine that can prevent almost completely the mammary tumor onset in HER-2/neu transgenic mice. In a translational perspective, the activity of the Triplex vaccine was also investigated against lung metastases showing that the vaccine is an effective treatment also for the cure of metastases. A future human application of the Triplex vaccine should take into account several aspects of biological behavior of the involved entities to improve the efficacy of therapeutic treatment and to try to predict, for example, the outcomes of longer experiments in order to move faster towards clinical phase I trials. To help to address this problem, MetastaSim, a hybrid Agent Based - ODE model for the simulation of the vaccine-elicited immune system response against lung metastases in mice is presented. The model is used as in silico wet-lab. As a first application MetastaSim is used to find protocols capable of maximizing the total number of prevented metastases, minimizing the number of vaccine administrations.</p> <p>Results</p> <p>The model shows that it is possible to obtain "in silico" a 45% reduction in the number of vaccinations. The analysis of the results further suggests that any optimal protocol for preventing lung metastases formation should be composed by an initial massive vaccine dosage followed by few vaccine recalls.</p> <p>Conclusions</p> <p>Such a reduction may represent an important result from the point of view of translational medicine to humans, since a downsizing of the number of vaccinations is usually advisable in order to minimize undesirable effects. The suggested vaccination strategy also represents a notable outcome. Even if this strategy is commonly used for many infectious diseases such as tetanus and hepatitis-B, it can be in fact considered as a relevant result in the field of cancer-vaccines immunotherapy. These results can be then used and verified in future "in vivo" experiments, and their outcome can be used to further improve and refine the model.</p

Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning

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

Induction of T-cell Memory by a Dendritic Cell Vaccine: a Computational Model

Motivation: Although results from phase III clinical trials substantially support the use of prophylactic and therapeutic vaccines against cancer, what has yet to be defined is how many and how frequent boosts are needed to sustain a long-lasting and protecting memory T-cell response against tumor antigens. Common experience is that such preclinical tests require the sacrifice of a relatively large number of animals, and are particularly time- and money-consuming. Results: As a first step to overcome these hurdles, we have developed an ordinary differential equation model that includes all relevant entities (such as activated cytotoxic T lymphocytes and memory T cells), and investigated the induction of immunological memory in the context of wild-type mice injected with a dendritic cell-based vaccine. We have simulated the biological behavior both in the presence and in the absence of memory T cells. Comparing results of ex vivo and in silico experiments, we show that the model is able to envisage the expansion and persistence of antigen-specific memory T cells. The model might be applicable to more complex vaccination schedules and substantially in any biological condition of prime-boosting. Availability and implementation: The model is fully described in the article. Contact: Supplementary information: Supplementary data are available at Bioinformatics online

Coupling of Petri Net Models of the Mycobacterial Infection Process and Innate Immune Response

Algorithms and the Foundations of Software technologyComputer Systems, Imagery and Medi

Antigenic diversity of dengue virus: implications for vaccine design

Ph.DDOCTOR OF PHILOSOPH

Computational modelling of mycobacterium infection and innate immune response in zebrafish

In this thesis we provided a comprehensive overview on the steps that are involved in the modeling process and simulation of biological phenomena; from the choice of the method to the validation of the results. We gradually implemented a model with which we would be able to study the complex interplay of the components involved in the Mycobacterium marinum infection process and innate immune response in zebrafish embryos. In itself this process is a model for deeper understanding of tuberculosis infection in humans using zebrafish as model organism. Each chapter is a building block in the modeling process, which gradually forms a model that can represent cause-and-effect among these components involved in the biological behavior.Computer Systems, Imagery and Medi

Predicting the outcomes of HIV treatment interruptions using computational modelling

In the past 30 years, HIV infection made a transition from fatal to chronic disease due to the emergence of potent treatment largely suppressing viral replication. However, this medication must be administered life-long on a regular basis to maintain viral suppression and is not always well tolerated. Any interruption of treatment causes residual virus to be reactivated and infection to progress, where the underlying processes occurring and consequences for the immune system are still poorly understood. Nonetheless, treatment interruptions are common due to adherence issues or limited access to antiretroviral drugs. Early clinical studies, aiming at application of treatment interruptions in a structured way, gave contradictory results concerning patient safety, discouraging further trials. In-silico models potentially add to knowledge but a review of the Literature indicates most current models used for studying treatment interruptions (equation-based), neglect recent clinical findings of collagen formation in lymphatic tissue due to HIV and its crucial role in immune system stability and efficacy. The aim of this research is the construction and application of so-called ‘Bottom-Up’ models to allow improved assessment of these processes in relation to HIV treatment interruptions. In this regard, a novel computational model based on 2D Cellular Automata for lymphatic tissue depletion and associated damage to the immune system was developed. Hence, (i) using this model, the influence of spatial distribution of collagen formation on HIV infection progression speed was evaluated while discussing aspects of computational performance. Further, (ii) direct Monte Carlo simulations were employed to explore the accumulation of tissue impairment due to repeated treatment interruptions and consequences for long-term prognosis. Finally, (iii) an inverse Monte Carlo approach was used to reconstruct yet unknown characteristics of patient groups. This is based on sparse data from past clinical studies on treatment interruptions with the aim of explaining their contradictory results