Computational modeling of immune system of the fish for a more effective vaccination in aquaculture

Madonia, Alice; Melchiorri, C; Bonamano, Simone; Marcelli, Marco; Bulfon, Chiara; Castiglione, Filippo; Galeotti, Marco; Volpatti, Donatella; Mosca, F; Tiscar, Pietro Giorgio; Romano, Nicla

doi:10.1093/bioinformatics/btx341

Cited by 27 publications

(22 citation statements)

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“…All together, M-T2D includes several models: (1) a model of energy balance and weight gain/loss is added in [ 45 ], based on the equations provided by [ 46 ] and [ 47 ]; (2) the emergence of the inflammation is described as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high-calorie intake [ 48 ]; (3) to better describe lifestyle, we include a previously published model of physical exercise [ 37 ]; (4) to counteract the inflammatory scenario, the presence of anti-inflammatory mechanisms promoted during exercise by skeletal muscle has been considered, based on a previous published study [ 49 ]; (5) finally, to describe the inflammatory status of the subject, M-T2D merges the metabolic model with a general-purpose simulator of the immune system [ 50 ], a modeling framework used to study different human pathologies [ 51 – 53 ], specific aspects of the immune response [ 54 , 55 ] and also aspects of non-human immunity [ 56 ].…”

Section: Methodsmentioning

confidence: 99%

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

Stolfi

Valentini²,

Palumbo

et al. 2020

BMC Bioinformatics

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Background The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. Results We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. Conclusions The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM.

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Section: Methodsmentioning

confidence: 99%

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

Stolfi

Valentini²,

Palumbo

et al. 2020

BMC Bioinformatics

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“…or EBV [21], [22]), cancer immuno-prevention [23], [24], type 1 hypersensitivity [25] and the chronic inflammation associated to type 2 diabetes [26] as well as specific aspects of the immune dynamics such as lymphocytes homing in lymph nodes [27], the gene regulation leading to cell differentiation [28], clonal dominance in heterologous immune responses [29] and also vaccination-eliciting fish immunity [30]. Relevant to the present study, the model has recently being used to simulate the response to a multi-epitope vaccine against SARS-CoV-2 [31], [32].…”

Section: Virus Definitionmentioning

confidence: 99%

From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling

Castiglione

Deb

Srivastava

et al. 2020

Preprint

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BackgroundImmune 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.AimThe dynamics of the insurgence of immunity is at the core of the both SARS-CoV-2 vaccine development and therapies. This paper addresses a fundamental question in the management of the infection: can we describe the insurgence (and the span) of immunity in COVID-19? The in-silico model developed here answers this question at individual (personalized) and population levels.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 the artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degree of immune competence. We use a parameter setting to reproduce known inter-patient variability and general epidemiological statistics.ResultsWe reproduce in-silico a number of clinical observations and we 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 is a prognostic factor for determining the clinical outcome of the infection.Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of simulating the immune response at individual and population levels. The model developed is able to explain and interpret observed patterns of infection and makes verifiable temporal predictions.Within the limitations imposed by the simulated environment, this work proposes in a quantitative way 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 i) show the power of model predictions, ii) identify the clinical end points that could be more suitable for computational modeling of COVID-19 immune response, iii) define the resolution and amount of data required to empower this class of models for translational medicine purposes and, iv) 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 immunesenescence.

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“…The model is a derivation of a well-established general-purpose immune system simulator [6], a modeling framework that has been used over the past two decades to study different human pathologies [7–9], specific aspects of the immune response [10, 11], and also nonhuman immunity [12].…”

Section: Computational Modelingmentioning

confidence: 99%

Modeling the Effect of High Calorie Diet on the Interplay between Adipose Tissue, Inflammation, and Diabetes

Prana

Tieri

Palumbo

et al. 2019

Computational and Mathematical Methods in Medicine

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Background. Type 2 diabetes (T2D) is a chronic metabolic disease potentially leading to serious widespread tissue damage. Human organism develops T2D when the glucose-insulin control is broken for reasons that are not fully understood but have been demonstrated to be linked to the emergence of a chronic inflammation. Indeed such low-level chronic inflammation affects the pancreatic production of insulin and triggers the development of insulin resistance, eventually leading to an impaired control of the blood glucose concentration. On the contrary, it is well-known that obesity and inflammation are strongly correlated. Aim. In this study, we investigate in silico the effect of overfeeding on the adipose tissue and the consequent set up of an inflammatory state. We model the emergence of the inflammation as the result of adipose mass increase which, in turn, is a direct consequence of a prolonged excess of high calorie intake. Results. The model reproduces the fat accumulation due to excessive caloric intake observed in two clinical studies. Moreover, while showing consistent weight gains over long periods of time, it reveals a drift of the macrophage population toward the proinflammatory phenotype, thus confirming its association with fatness.

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Computational modeling of immune system of the fish for a more effective vaccination in aquaculture

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 27 publications

References 46 publications

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling

Modeling the Effect of High Calorie Diet on the Interplay between Adipose Tissue, Inflammation, and Diabetes

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