scholarly journals Comparative Computational Modeling of the Bat and Human Immune Response to Viral Infection with the Comparative Biology Immune Agent Based Model

Viruses ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1620
Author(s):  
Chase Cockrell ◽  
Gary An
Keyword(s):  
Viral Infection ◽  
Viral Infections ◽  
Comparative Modeling ◽  
Initial Study ◽  
Comparative Biology ◽  
Agent Based Model ◽  
Agent Based ◽  
Human Immune ◽  
The Impact ◽  
Inflammasome Activity

Given the impact of pandemics due to viruses of bat origin, there is increasing interest in comparative investigation into the differences between bat and human immune responses. The practice of comparative biology can be enhanced by computational methods used for dynamic knowledge representation to visualize and interrogate the putative differences between the two systems. We present an agent based model that encompasses and bridges differences between bat and human responses to viral infection: the comparative biology immune agent based model, or CBIABM. The CBIABM examines differences in innate immune mechanisms between bats and humans, specifically regarding inflammasome activity and type 1 interferon dynamics, in terms of tolerance to viral infection. Simulation experiments with the CBIABM demonstrate the efficacy of bat-related features in conferring viral tolerance and also suggest a crucial role for endothelial inflammasome activity as a mechanism for bat systemic viral tolerance and affecting the severity of disease in human viral infections. We hope that this initial study will inspire additional comparative modeling projects to link, compare, and contrast immunological functions shared across different species, and in so doing, provide insight and aid in preparation for future viral pandemics of zoonotic origin.

scholarly journals Comparative computational modeling of the bat and human immune response to viral infection with the Comparative Biology Immune Agent Based Model

2021 ◽  
Author(s):  
Chase Cockrell ◽  
Gary An
Keyword(s):  
Viral Infection ◽  
Viral Infections ◽  
Comparative Modeling ◽  
Initial Study ◽  
Comparative Biology ◽  
Agent Based Model ◽  
Agent Based ◽  
Human Immune ◽  
The Impact ◽  
Inflammasome Activity

Given the impact of pandemics due to viruses of bat origin there is increasing interest in comparative investigation into the differences between bat and human immune responses. The practice of comparative biology can be enhanced by computational methods used for dynamic knowledge representation to visualize and interrogate the putative differences between the two systems. We present an agent-based model that encompasses and bridges the differences between bat and human responses to viral infection: the Comparative Biology Immune Agent-based Model, or CBIABM. The CBIABM examines differences in innate immune mechanisms between bats and humans, specifically regarding inflammasome activity and Type 1 Interferon dynamics, in terms of tolerance to viral infection. Simulation experiments with the CBIABM demonstrate the efficacy of bat-related features in conferring viral tolerance and also suggest a crucial role for endothelial inflammasome activity as a mechanism for bat systemic viral tolerance and affecting the severity of disease in human viral infections. We hope that this initial study will inspire additional comparative modeling projects to link, compare, and contrast immunological functions shared across different species, and in so doing, provide insight and aid in the preparation for future viral pandemics of zoonotic origin.

scholarly journals The Cellular Immunity Agent Based Model (CIABM): Replicating the cellular immune response to viral respiratory infection

2019 ◽  
Author(s):  
Andrew Becker ◽  
Gary An ◽  
Chase Cockrell
Keyword(s):  
Immune Response ◽  
Viral Infection ◽  
Respiratory Infection ◽  
Viral Infections ◽  
Immune Memory ◽  
Agent Based Model ◽  
Agent Based ◽  
Viral Respiratory Infection ◽  
Cellular Immune ◽  
Viral Respiratory

AbstractViral respiratory infections, such as influenza, result in over 1 million deaths worldwide each year. To date, there are few therapeutic interventions able to affect the course of the disease once acquired, a deficit with stark consequences that were readily evident in the current COVID-19 pandemic. We present the Cellular Immune Agent Based Model (CIABM) as a flexible framework for modeling acute viral infection and cellular immune memory development. The mechanism/rule-based nature of the CIABM allows for interrogation of the complex dynamics of the human immune system during various types of viral infections. The CIABM is an extension of a prior agent-based model of the innate immune response, incorporating additional cellular types and mediators involved in the response to viral infection. The CIABM simulates the dynamics of viral respiratory infection in terms of epithelial invasion, immune cellular population changes and cytokine measurements. Validation of the CIABM involved effectively replicating in vivo measurements of circulating mediator levels from a clinical cohort of influenza patients. The general purpose nature of the CIABM allows for both the representation of various types of known viral infections and facilitates the exploration of hypothetical, novel viral pathogens.

scholarly journals Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonatan Almagor ◽  
Stefano Picascia
Keyword(s):  
High Rate ◽  
Contact Tracing ◽  
Agent Based Model ◽  
Agent Based ◽  
The Social ◽  
Behavioural Factors ◽  
The Impact ◽  
Different Levels ◽  
User Rate

AbstractA contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.

scholarly journals Virus-mediated archaeal hecatomb in the deep seafloor

2016 ◽  
Vol 2 (10) ◽  
pp. e1600492 ◽  
Author(s):  
Roberto Danovaro ◽  
Antonio Dell’Anno ◽  
Cinzia Corinaldesi ◽  
Eugenio Rastelli ◽  
Ricardo Cavicchioli ◽  
...  
Keyword(s):  
Viral Infection ◽  
Deep Sea ◽  
Viral Infections ◽  
Biogeochemical Cycles ◽  
Global Scale ◽  
Viral Lysis ◽  
Group I ◽  
Global Biogeochemical Cycles ◽  
Bacterial Genes ◽  
The Impact

Viruses are the most abundant biological entities in the world’s oceans, and they play a crucial role in global biogeochemical cycles. In deep-sea ecosystems, archaea and bacteria drive major nutrient cycles, and viruses are largely responsible for their mortality, thereby exerting important controls on microbial dynamics. However, the relative impact of viruses on archaea compared to bacteria is unknown, limiting our understanding of the factors controlling the functioning of marine systems at a global scale. We evaluate the selectivity of viral infections by using several independent approaches, including an innovative molecular method based on the quantification of archaeal versus bacterial genes released by viral lysis. We provide evidence that, in all oceanic surface sediments (from 1000- to 10,000-m water depth), the impact of viral infection is higher on archaea than on bacteria. We also found that, within deep-sea benthic archaea, the impact of viruses was mainly directed at members of specific clades of Marine Group I Thaumarchaeota. Although archaea represent, on average, ~12% of the total cell abundance in the top 50 cm of sediment, virus-induced lysis of archaea accounts for up to one-third of the total microbial biomass killed, resulting in the release of ~0.3 to 0.5 gigatons of carbon per year globally. Our results indicate that viral infection represents a key mechanism controlling the turnover of archaea in surface deep-sea sediments. We conclude that interactions between archaea and their viruses might play a profound, previously underestimated role in the functioning of deep-sea ecosystems and in global biogeochemical cycles.

scholarly journals Examining the Impact of the Walking School Bus With an Agent-Based Model

2014 ◽  
Vol 104 (7) ◽  
pp. 1196-1203 ◽  
Author(s):  
Yong Yang ◽  
Ana Diez-Roux ◽  
Kelly R. Evenson ◽  
Natalie Colabianchi
Keyword(s):  
Agent Based Model ◽  
School Bus ◽  
Agent Based ◽  
Walking School Bus ◽  
The Impact

scholarly journals A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

2019 ◽  
Vol 1 (1) ◽  
pp. 11-20
Author(s):  
Marija Majda Perisic ◽  
Tomislav Martinec ◽  
Mario Storga ◽  
John S Gero
Keyword(s):  
Computational Study ◽  
Space Exploration ◽  
Solution Space ◽  
Problem Solution ◽  
Design Team ◽  
Design Teams ◽  
Agent Based Model ◽  
Problem Space ◽  
Agent Based ◽  
The Impact

AbstractThis paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results.

scholarly journals Development of an agent-based model to assess the impact of substandard and falsified anti-malarials: Uganda case study

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Sachiko Ozawa ◽  
Daniel R. Evans ◽  
Colleen R. Higgins ◽  
Sarah K. Laing ◽  
Phyllis Awor
Keyword(s):  
Agent Based Model ◽  
Agent Based ◽  
The Impact
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