42 research outputs found
Addressing current challenges in cancer immunotherapy with mathematical and computational modeling
The goal of cancer immunotherapy is to boost a patient's immune response to a
tumor. Yet, the design of an effective immunotherapy is complicated by various
factors, including a potentially immunosuppressive tumor microenvironment,
immune-modulating effects of conventional treatments, and therapy-related
toxicities. These complexities can be incorporated into mathematical and
computational models of cancer immunotherapy that can then be used to aid in
rational therapy design. In this review, we survey modeling approaches under
the umbrella of the major challenges facing immunotherapy development, which
encompass tumor classification, optimal treatment scheduling, and combination
therapy design. Although overlapping, each challenge has presented unique
opportunities for modelers to make contributions using analytical and numerical
analysis of model outcomes, as well as optimization algorithms. We discuss
several examples of models that have grown in complexity as more biological
information has become available, showcasing how model development is a dynamic
process interlinked with the rapid advances in tumor-immune biology. We
conclude the review with recommendations for modelers both with respect to
methodology and biological direction that might help keep modelers at the
forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society
Interfac
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
A novel paradigm for cell and molecule interaction ontology: from the CMM model to IMGT-ONTOLOGY
International audienc
Theoretical Modeling Techniques and Their Impact on Tumor Immunology
Currently, cancer is one of the leading causes of death in industrial nations. While conventional cancer treatment usually results in the patient suffering from severe side effects, immunotherapy is a promising alternative. Nevertheless, some questions remain unanswered with regard to using immunotherapy to treat cancer hindering it from being widely established. To help rectify this deficit in knowledge, experimental data, accumulated from a huge number of different studies, can be integrated into theoretical models of the tumor-immune system interaction. Many complex mechanisms in immunology and oncology cannot be measured in experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of tumor-immune system interactions can be explored and clinical treatment schedules optimized to lower both tumor burden and side effects. In this paper, we aim to explain the main mathematical and computational modeling techniques used in tumor immunology to experimental researchers and clinicians. In addition, we review relevant published work and provide an overview of its impact to the field
Computational modeling of immune system of the fish for a more effective vaccination in aquaculture
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Parallel BioScape: A Stochastic and Parallel Language for Mobile and Spatial Interactions
BioScape is a concurrent language motivated by the biological landscapes
found at the interface of biology and biomaterials. It has been motivated by
the need to model antibacterial surfaces, biofilm formation, and the effect of
DNAse in treating and preventing biofilm infections. As its predecessor, SPiM,
BioScape has a sequential semantics based on Gillespie's algorithm, and its
implementation does not scale beyond 1000 agents. However, in order to model
larger and more realistic systems, a semantics that may take advantage of the
new multi-core and GPU architectures is needed. This motivates the introduction
of parallel semantics, which is the contribution of this paper: Parallel
BioScape, an extension with fully parallel semantics.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347
Computational modelling approaches to vaccinology
Excepting the Peripheral and Central Nervous Systems, the Immune System is the most complex of somatic systems in higher animals. This complexity manifests itself at many levels from the molecular to that of the whole organism. Much insight into this confounding complexity can be gained through computational simulation. Such simulations range in application from epitope prediction through to the modelling of vaccination strategies. In this review, we evaluate selectively various key applications relevant to computational vaccinology: these include technique that operates at different scale that is, from molecular to organisms and even to population level
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