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Parallelisation strategies for agent based simulation of immune systems. BMC Bioinformatics 2019; 20:579. [PMID: 31823716 PMCID: PMC6905091 DOI: 10.1186/s12859-019-3181-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 10/29/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches. RESULTS This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment. CONCLUSIONS Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware.
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Presbitero A, Mancini E, Castiglione F, Krzhizhanovskaya VV, Quax R. Game of neutrophils: modeling the balance between apoptosis and necrosis. BMC Bioinformatics 2019; 20:475. [PMID: 31823711 PMCID: PMC6905093 DOI: 10.1186/s12859-019-3044-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 08/21/2019] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Neutrophils are one of the key players in the human innate immune system (HIIS). In the event of an insult where the body is exposed to inflammation triggering moieties (ITMs), neutrophils are mobilized towards the site of insult and antagonize the inflammation. If the inflammation is cleared, neutrophils go into a programmed death called apoptosis. However, if the insult is intense or persistent, neutrophils take on a violent death pathway called necrosis, which involves the rupture of their cytoplasmic content into the surrounding tissue that causes local tissue damage, thus further aggravating inflammation. This seemingly paradoxical phenomenon fuels the inflammatory process by triggering the recruitment of additional neutrophils to the site of inflammation, aimed to contribute to the complete neutralization of severe inflammation. This delicate balance between the cost and benefit of the neutrophils' choice of death pathway has been optimized during the evolution of the innate immune system. The goal of our work is to understand how the tradeoff between the cost and benefit of the different death pathways of neutrophils, in response to various levels of insults, has been optimized over evolutionary time by using the concepts of evolutionary game theory. RESULTS We show that by using evolutionary game theory, we are able to formulate a game that predicts the percentage of necrosis and apoptosis when exposed to various levels of insults. CONCLUSION By adopting an evolutionary perspective, we identify the driving mechanisms leading to the delicate balance between apoptosis and necrosis in neutrophils' cell death in response to different insults. Using our simple model, we verify that indeed, the global cost of remaining ITMs is the driving mechanism that reproduces the percentage of necrosis and apoptosis observed in data and neutrophils need sufficient information of the overall inflammation to be able to pick a death pathway that presumably increases the survival of the organism.
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Affiliation(s)
- Alva Presbitero
- ITMO University, Saint Petersburg, Russian Federation.
- University of Amsterdam, Amsterdam, the Netherlands.
| | | | - Filippo Castiglione
- University of Amsterdam, Amsterdam, the Netherlands
- IAC- National Research Council of Italy, Rome, Italy
| | - Valeria V Krzhizhanovskaya
- ITMO University, Saint Petersburg, Russian Federation
- University of Amsterdam, Amsterdam, the Netherlands
| | - Rick Quax
- University of Amsterdam, Amsterdam, the Netherlands
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Abstract
Therapeutic protein drugs have significantly improved the management of many severe and chronic diseases. However, their development and optimal clinical application are complicated by the induction of unwanted immune responses. Therapeutic protein-induced antidrug antibodies can alter drug pharmacokinetics and pharmacodynamics leading to impaired efficacy and occasionally serious safety issues. There has been a growing interest over the past decade in developing methods to assess the risk of unwanted immunogenicity during preclinical drug development, with the aim to mitigate the risk during the molecular design phase, clinical development and when products reach the market. Here, we discuss approaches to therapeutic protein immunogenicity risk assessment, with attention to assays and in vivo models used to mitigate this risk.
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Norton KA, Gong C, Jamalian S, Popel AS. Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment. Processes (Basel) 2019; 7:37. [PMID: 30701168 PMCID: PMC6349239 DOI: 10.3390/pr7010037] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.
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Affiliation(s)
- Kerri-Ann Norton
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Computer Science Program, Department of Science, Mathematics, and Computing, Bard College, Annandale-on-Hudson, NY 12504, USA
| | - Chang Gong
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Samira Jamalian
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
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Sauna ZE, Lagassé D, Pedras-Vasconcelos J, Golding B, Rosenberg AS. Evaluating and Mitigating the Immunogenicity of Therapeutic Proteins. Trends Biotechnol 2018; 36:1068-1084. [DOI: 10.1016/j.tibtech.2018.05.008] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2018] [Revised: 05/18/2018] [Accepted: 05/22/2018] [Indexed: 12/19/2022]
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Liberman A, Kario D, Mussel M, Brill J, Buetow K, Efroni S, Nevo U. Cell studio: A platform for interactive, 3D graphical simulation of immunological processes. APL Bioeng 2018; 2:026107. [PMID: 31069304 PMCID: PMC6481718 DOI: 10.1063/1.5039473] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 05/04/2018] [Indexed: 12/27/2022] Open
Abstract
The field of computer modeling and simulation of biological systems is rapidly advancing, backed by significant progress in the fields of experimentation techniques, computer hardware, and programming software. The result of a simulation may be delivered in several ways, from numerical results, through graphs of the simulated run, to a visualization of the simulation. The vision of an in-silico experiment mimicking an in-vitro or in-vivo experiment as it is viewed under a microscope is appealing but technically demanding and computationally intensive. Here, we report “Cell Studio,” a generic, hybrid platform to simulate an immune microenvironment with biological and biophysical rules. We use game engines—generic programs for game creation which offer ready-made assets and tools—to create a visualized, interactive 3D simulation. We also utilize a scalable architecture that delegates the computational load to a server. The user may view the simulation, move the “camera” around, stop, fast-forward, and rewind it and inject soluble molecules into the extracellular medium at any point in time. During simulation, graphs are created in real time for a broad view of system-wide processes. The model is parametrized using a user-friendly Graphical User Interface (GUI). We show a simple validation simulation and compare its results with those from a “classical” simulation, validated against a “wet” experiment. We believe that interactive, real-time 3D visualization may aid in generating insights from the model and encourage intuition about the immunological scenario.
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Affiliation(s)
- Asaf Liberman
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | | | - Matan Mussel
- Physics Department, TU Dortmund University, Dortmund 44227, Germany
| | - Jacob Brill
- Arizona State University, Tempe, Arizona 85281, USA
| | | | - Sol Efroni
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan 52900, Israel
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Castiglione F, Tieri P, Palma A, Jarrah AS. Statistical ensemble of gene regulatory networks of macrophage differentiation. BMC Bioinformatics 2016; 17:506. [PMID: 28155642 PMCID: PMC5260144 DOI: 10.1186/s12859-016-1363-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Macrophages cover a major role in the immune system, being the most plastic cell yielding several key immune functions. METHODS Here we derived a minimalistic gene regulatory network model for the differentiation of macrophages into the two phenotypes M1 (pro-) and M2 (anti-inflammatory). RESULTS To test the model, we simulated a large number of such networks as in a statistical ensemble. In other words, to enable the inter-cellular crosstalk required to obtain an immune activation in which the macrophage plays its role, the simulated networks are not taken in isolation but combined with other cellular agents, thus setting up a discrete minimalistic model of the immune system at the microscopic/intracellular (i.e., genetic regulation) and mesoscopic/intercellular scale. CONCLUSIONS We show that within the mesoscopic level description of cellular interaction and cooperation, the gene regulatory logic is coherent and contributes to the overall dynamics of the ensembles that shows, statistically, the expected behaviour.
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Affiliation(s)
- Filippo Castiglione
- Institute for Applied Computing, National Research Council of Italy, Via dei Taurini 19, Rome, 00185 Italy
| | - Paolo Tieri
- Institute for Applied Computing, National Research Council of Italy, Via dei Taurini 19, Rome, 00185 Italy
| | - Alessandro Palma
- Department of Biology, University of Tor Vergata, Via della ricerca scientifica 1, Rome, 00133 Italy
| | - Abdul Salam Jarrah
- Department of Mathematics and Statistics, American University of Sharjah, P.O.Box 26666, Sharjah, UAE
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Mathematical Models for Immunology: Current State of the Art and Future Research Directions. Bull Math Biol 2016; 78:2091-2134. [PMID: 27714570 PMCID: PMC5069344 DOI: 10.1007/s11538-016-0214-9] [Citation(s) in RCA: 80] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 09/26/2016] [Indexed: 01/01/2023]
Abstract
The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.
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Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data. PLoS One 2015; 10:e0141295. [PMID: 26535589 PMCID: PMC4633145 DOI: 10.1371/journal.pone.0141295] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 10/07/2015] [Indexed: 12/04/2022] Open
Abstract
Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.
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Wells DK, Chuang Y, Knapp LM, Brockmann D, Kath WL, Leonard JN. Spatial and functional heterogeneities shape collective behavior of tumor-immune networks. PLoS Comput Biol 2015; 11:e1004181. [PMID: 25905470 PMCID: PMC4408028 DOI: 10.1371/journal.pcbi.1004181] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2014] [Accepted: 02/06/2015] [Indexed: 12/31/2022] Open
Abstract
Tumor growth involves a dynamic interplay between cancer cells and host cells, which collectively form a tumor microenvironmental network that either suppresses or promotes tumor growth under different conditions. The transition from tumor suppression to tumor promotion is mediated by a tumor-induced shift in the local immune state, and despite the clinical challenge this shift poses, little is known about how such dysfunctional immune states are initiated. Clinical and experimental observations have indicated that differences in both the composition and spatial distribution of different cell types and/or signaling molecules within the tumor microenvironment can strongly impact tumor pathogenesis and ultimately patient prognosis. How such “functional” and “spatial” heterogeneities confer such effects, however, is not known. To investigate these phenomena at a level currently inaccessible by direct observation, we developed a computational model of a nascent metastatic tumor capturing salient features of known tumor-immune interactions that faithfully recapitulates key features of existing experimental observations. Surprisingly, over a wide range of model formulations, we observed that heterogeneity in both spatial organization and cell phenotype drove the emergence of immunosuppressive network states. We determined that this observation is general and robust to parameter choice by developing a systems-level sensitivity analysis technique, and we extended this analysis to generate other parameter-independent, experimentally testable hypotheses. Lastly, we leveraged this model as an in silico test bed to evaluate potential strategies for engineering cell-based therapies to overcome tumor associated immune dysfunction and thereby identified modes of immune modulation predicted to be most effective. Collectively, this work establishes a new integrated framework for investigating and modulating tumor-immune networks and provides insights into how such interactions may shape early stages of tumor formation. Over the course of tumor growth, cancer cells interact with normal cells via processes that are difficult to understand by experiment alone. This challenge is particularly pronounced at early stages of tumor formation, when experimental observation is most limited. Elucidating such interactions could inform both understanding of cancer and clinical practice. To address this need we developed a computational model capturing the current understanding of how individual metastatic tumor cells and immune cells sense and contribute to the tumor environment, which in turn enabled us to investigate the complex, collective behavior of these systems. Surprisingly, we discovered that tumor escape from immune control was enhanced by the existence of small differences (or heterogeneities) in the responses of individual immune cells to their environment, as well as by heterogeneities in the way that cells and the molecules they secrete are arranged in space. These conclusions held true over a range of model formulations, suggesting that this is a general feature of these tumor-immune networks. Finally, we used this model as a test bed to evaluate potential strategies for enhancing immunological control of early tumors, ultimately predicting that specifically modulating tumor-associated immune dysfunction may be more effective than simply enhanced tumor killing.
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Affiliation(s)
- Daniel K. Wells
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
- Northwestern University Physical Sciences-Oncology Center, Evanston, Illinois, United States of America
| | - Yishan Chuang
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Louis M. Knapp
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Dirk Brockmann
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
- Northwestern University Physical Sciences-Oncology Center, Evanston, Illinois, United States of America
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, Illinois, United States of America
- Northwestern Institute on Complex Science, Northwestern University, Evanston, Illinois, United States of America
- Institute for Theoretical Biology, Humboldt University Berlin, Berlin, Germany
| | - William L. Kath
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America
- Northwestern University Physical Sciences-Oncology Center, Evanston, Illinois, United States of America
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, Illinois, United States of America
- Northwestern Institute on Complex Science, Northwestern University, Evanston, Illinois, United States of America
- Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois, United States of America
| | - Joshua N. Leonard
- Northwestern University Physical Sciences-Oncology Center, Evanston, Illinois, United States of America
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois, United States of America
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, Illinois, United States of America
- Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois, United States of America
- * E-mail:
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A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins: part 1-theoretical model. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2014; 3:e133. [PMID: 25184733 PMCID: PMC4211265 DOI: 10.1038/psp.2014.30] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2013] [Accepted: 05/19/2014] [Indexed: 12/26/2022]
Abstract
A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins was formulated by recapitulating key biological mechanisms, including antigen presentation, activation, proliferation, and differentiation of immune cells, secretion of antidrug antibodies (ADA), as well as in vivo disposition of ADA and therapeutic proteins. This system-level model contains three scales: a subcellular level representing antigen presentation processes by dendritic cells; a cellular level accounting for cell kinetics during humoral immune response; and a whole-body level accounting for therapeutic protein in vivo disposition. The model simulations for in vivo responses against antigenic protein challenge are consistent with many known immunological observations. By simulating immune responses under various initial parameter conditions, the model suggests hypotheses for future experimental investigation and contributes to the mechanistic understanding of immunogenicity. With future experimental validation, this model may potentially provide a platform to generate and test hypotheses about immunogenicity risk assessment and ultimately aid in immunogenicity prediction.
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Tauber AI. Reconceiving autoimmunity: An overview. J Theor Biol 2014; 375:52-60. [PMID: 24880023 DOI: 10.1016/j.jtbi.2014.05.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Revised: 05/13/2014] [Accepted: 05/20/2014] [Indexed: 10/25/2022]
Abstract
Three interconnected positions are advocated: (1) although serving as a useful model, the immune self does not exist as such; (2) instead of a self/nonself demarcation, the immune system 'sees' itself, i.e., it does not ignore the 'self' or attack the 'other;' but exhibits a spectrum of responses, which when viewed from outside the system appear as discrimination of 'self' and 'nonself' based on certain criteria of reactivity. When immune reactions are conceived in terms of normal physiology and open exchange with the environment, where borders dividing host and foreign are elusive and changing, host defense is only part of the immune system's functions, which actually comprise two basic tasks: protection, i.e., to preserve host integrity, and maintenance of organismic identity. And thus (3) if the spectrum of immunity is enlarged, differentiating low reactive 'autoimmune' reactions from activated immune responses against the 'other' is only a matter of degree. Simply, all immunity is 'autoimmunity,' and the pathologic state of immunity directed at normal constituents of the organism is a particular case of dis-regulation, which appropriately is designated, autoimmune. Other uses of 'autoimmunity' and its congeners function as the semantic remnants of Burnet's original self/nonself theory and should be replaced. A new nomenclature is proposed, concinnity, which more accurately designates the physiology of the animal's ordinary housekeeping economy mediated by the immune system than 'autoimmunity' when used to describe such normal functions.
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Affiliation(s)
- Alfred I Tauber
- Cohn Institute for the History and Philosophy of Science and Ideas, Tel Aviv University, Tel Aviv, Israel.
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Pappalardo F, Pennisi M, Ricupito A, Topputo F, Bellone M. Induction of T-cell memory by a dendritic cell vaccine: a computational model. Bioinformatics 2014; 30:1884-91. [DOI: 10.1093/bioinformatics/btu059] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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Castiglione F, Diaz V, Gaggioli A, Liò P, Mazzà C, Merelli E, Meskers CGM, Pappalardo F, von Ammon R. Physio-environmental sensing and live modeling. Interact J Med Res 2013; 2:e3. [PMID: 23612245 PMCID: PMC3628118 DOI: 10.2196/ijmr.2092] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2012] [Revised: 06/15/2012] [Accepted: 08/01/2012] [Indexed: 11/13/2022] Open
Abstract
In daily life, humans are constantly interacting with their environment. Evidence is emerging that this interaction is a very important modulator of health and well-being, even more so in our rapidly ageing society. Information and communication technology lies at the heart of the human health care revolution. It cannot remain acceptable to use out of date data analysis and predictive algorithms when superior alternatives exist. Communication network speed, high penetration of home broadband, availability of various mobile network options, together with the available detailed biological data for individuals, are producing promising advances in computerized systems that will turn information on human-environment interactions into actual knowledge with the potential to help make medical and lifestyle decisions. We introduced and discussed a key scenario in which hardware and software technologies capable of simultaneously sensing physiological and environmental signals process health care data in real-time to issue alarms, warnings, or simple recommendations to the patient or carers.
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Affiliation(s)
- Filippo Castiglione
- Istituto per le Applicazioni del Calcolo, National Research Council of Italy, Rome, Italy.
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Frangi AF, Hose DR, Hunter PJ, Ayache N, Brooks D. Special issue on medical imaging and image computing in computational physiology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1-7. [PMID: 23409282 DOI: 10.1109/tmi.2012.2234320] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
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Spatial Aspects of HIV Infection. LECTURE NOTES ON MATHEMATICAL MODELLING IN THE LIFE SCIENCES 2013. [DOI: 10.1007/978-1-4614-4178-6_1] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Abstract
Software agents are particularly suitable for engineering models and simulations of cellular systems. In a very natural and intuitive manner, individual software components are therein delegated to reproduce "in silico" the behavior of individual components of alive systems at a given level of resolution. Individuals' actions and interactions among individuals allow complex collective behavior to emerge. In this chapter we first introduce the readers to software agents and multi-agent systems, reviewing the evolution of agent-based modeling of biomolecular systems in the last decade. We then describe the main tools, platforms, and methodologies available for programming societies of agents, possibly profiting also of toolkits that do not require advanced programming skills.
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Affiliation(s)
- Nicola Cannata
- School of Science and Technology, University of Camerino, Camerino, Italy.
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Bianca C, Chiacchio F, Pappalardo F, Pennisi M. Mathematical modeling of the immune system recognition to mammary carcinoma antigen. BMC Bioinformatics 2012; 13 Suppl 17:S21. [PMID: 23281916 PMCID: PMC3521211 DOI: 10.1186/1471-2105-13-s17-s21] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The definition of artificial immunity, realized through vaccinations, is nowadays a practice widely developed in order to eliminate cancer disease. The present paper deals with an improved version of a mathematical model recently analyzed and related to the competition between immune system cells and mammary carcinoma cells under the action of a vaccine (Triplex). The model describes in detail both the humoral and cellular response of the immune system to the tumor associate antigen and the recognition process between B cells, T cells and antigen presenting cells. The control of the tumor cells growth occurs through the definition of different vaccine protocols. The performed numerical simulations of the model are in agreement with in vivo experiments on transgenic mice.
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Affiliation(s)
- Carlo Bianca
- Dipartimento di Matematica e Informatica, Università di Catania, Catania, Italy
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Calonaci C, Chiacchio F, Pappalardo F. Optimal vaccination schedule search using genetic algorithm over MPI technology. BMC Med Inform Decis Mak 2012; 12:129. [PMID: 23148787 PMCID: PMC3558354 DOI: 10.1186/1472-6947-12-129] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2012] [Accepted: 11/01/2012] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule. METHODS To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule. RESULTS & CONCLUSIONS The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented.
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Narang V, Decraene J, Wong SY, Aiswarya BS, Wasem AR, Leong SR, Gouaillard A. Systems immunology: a survey of modeling formalisms, applications and simulation tools. Immunol Res 2012; 53:251-65. [PMID: 22528121 DOI: 10.1007/s12026-012-8305-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Alemani D, Pappalardo F, Pennisi M, Motta S, Brusic V. Combining cellular automata and Lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition. J Immunol Methods 2011; 376:55-68. [PMID: 22154892 DOI: 10.1016/j.jim.2011.11.009] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2011] [Revised: 11/21/2011] [Accepted: 11/21/2011] [Indexed: 01/22/2023]
Abstract
In the last decades the Lattice Boltzmann method (LB) has been successfully used to simulate a variety of processes. The LB model describes the microscopic processes occurring at the cellular level and the macroscopic processes occurring at the continuum level with a unique function, the probability distribution function. Recently, it has been tried to couple deterministic approaches with probabilistic cellular automata (probabilistic CA) methods with the aim to model temporal evolution of tumor growths and three dimensional spatial evolution, obtaining hybrid methodologies. Despite the good results attained by CA-PDE methods, there is one important issue which has not been completely solved: the intrinsic stochastic nature of the interactions at the interface between cellular (microscopic) and continuum (macroscopic) level. CA methods are able to cope with the stochastic phenomena because of their probabilistic nature, while PDE methods are fully deterministic. Even if the coupling is mathematically correct, there could be important statistical effects that could be missed by the PDE approach. For such a reason, to be able to develop and manage a model that takes into account all these three level of complexity (cellular, molecular and continuum), we believe that PDE should be replaced with a statistic and stochastic model based on the numerical discretization of the Boltzmann equation: The Lattice Boltzmann (LB) method. In this work we introduce a new hybrid method to simulate tumor growth and immune system, by applying Cellular Automata Lattice Boltzmann (CA-LB) approach.
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Pappalardo F, Forero IM, Pennisi M, Palazon A, Melero I, Motta S. SimB16: modeling induced immune system response against B16-melanoma. PLoS One 2011; 6:e26523. [PMID: 22028894 PMCID: PMC3197530 DOI: 10.1371/journal.pone.0026523] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2011] [Accepted: 09/28/2011] [Indexed: 01/29/2023] Open
Abstract
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.
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Affiliation(s)
| | | | | | - Asis Palazon
- CIMA and CUN University of Navarra Pamplona, Pamplona, Spain
| | - Ignacio Melero
- CIMA and CUN University of Navarra Pamplona, Pamplona, Spain
- * E-mail:
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Tong JC, Ng LFP. Understanding infectious agents from an in silico perspective. Drug Discov Today 2010; 16:42-9. [PMID: 20974283 PMCID: PMC7185741 DOI: 10.1016/j.drudis.2010.10.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2010] [Revised: 10/01/2010] [Accepted: 10/18/2010] [Indexed: 12/31/2022]
Abstract
Knowledge of infectious diseases now emerging from genomic, proteomic, epidemiological and clinical data can provide insights into the mechanisms of immune function, disease pathogenesis and epidemiology. Here, we describe how considerable advances in computational methods of data mining, mathematical modeling in epidemiology and simulation have been used to enhance our understanding of infectious agents and discuss their impact on the discovery of new therapeutics and controlling their spread.
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Affiliation(s)
- Joo Chuan Tong
- Data Mining Department, Institute for Infocomm Research, 1 Fusionopolis Way, 21-01 Connexis South Tower, Singapore 138632, Singapore.
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Pennisi M, Pappalardo F, Palladini A, Nicoletti G, Nanni P, Lollini PL, Motta S. Modeling the competition between lung metastases and the immune system using agents. BMC Bioinformatics 2010; 11 Suppl 7:S13. [PMID: 21106120 PMCID: PMC2957681 DOI: 10.1186/1471-2105-11-s7-s13] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND 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. RESULTS 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. CONCLUSIONS 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.
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Affiliation(s)
- Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, Catania, Italy
| | - Francesco Pappalardo
- Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, Catania, Italy
| | - Ariannna Palladini
- Laboratory of Immunology and Biology of Metastasis, Cancer Research Section, Department of Experimental Pathology, University of Bologna, Bologna, Italy
| | - Giordano Nicoletti
- Laboratory of Experimental Oncology, Rizzoli Orthopedic Institute, Bologna, Italy
| | - Patrizia Nanni
- Laboratory of Immunology and Biology of Metastasis, Cancer Research Section, Department of Experimental Pathology, University of Bologna, Bologna, Italy
| | - Pier-Luigi Lollini
- Department of Hematology and Oncologic Sciences "L. e A. Seragnoli", University of Bologna, Bologna, Italy
| | - Santo Motta
- Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, Catania, Italy
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Viceconti M, Kohl P. The virtual physiological human: computer simulation for integrative biomedicine I. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2010; 368:2591-2594. [PMID: 20439263 DOI: 10.1098/rsta.2010.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Affiliation(s)
- Marco Viceconti
- Laboratorio di Tecnologia Medica, Istituto Ortopedico Rizzoli, Bologna, Italy.
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