A three-dimensional multi-agent-based model for the evolution of Chagas’ disease

Biosystems ◽  
2010 ◽  
Vol 100 (3) ◽  
pp. 225-230 ◽  
Author(s):  
Viviane Galvão ◽  
José Garcia Vivas Miranda
Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5314
Author(s):  
Kathleen M. Storey ◽  
Trachette L. Jackson

Oncolytic viral therapies and immunotherapies are of growing clinical interest due to their selectivity for tumor cells over healthy cells and their immunostimulatory properties. These treatment modalities provide promising alternatives to the standard of care, particularly for cancers with poor prognoses, such as the lethal brain tumor glioblastoma (GBM). However, uncertainty remains regarding optimal dosing strategies, including how the spatial location of viral doses impacts therapeutic efficacy and tumor landscape characteristics that are most conducive to producing an effective immune response. We develop a three-dimensional agent-based model (ABM) of GBM undergoing treatment with a combination of an oncolytic Herpes Simplex Virus and an anti-PD-1 immunotherapy. We use a mechanistic approach to model the interactions between distinct populations of immune cells, incorporating both innate and adaptive immune responses to oncolytic viral therapy and including a mechanism of adaptive immune suppression via the PD-1/PD-L1 checkpoint pathway. We utilize the spatially explicit nature of the ABM to determine optimal viral dosing in both the temporal and spatial contexts. After proposing an adaptive viral dosing strategy that chooses to dose sites at the location of highest tumor cell density, we find that, in most cases, this adaptive strategy produces a more effective treatment outcome than repeatedly dosing in the center of the tumor.


Author(s):  
John Wu ◽  
David Ben-Arieh ◽  
Zhenzhen Shi

This research proposes an agent-based simulation model combined with the strength of systemic dynamic mathematical model, providing a new modeling and simulation approach of the pathogenesis of AIR. AIR is the initial stage of a typical sepsis episode, often leading to severe sepsis or septic shocks. The process of AIR has been in the focal point affecting more than 750,000 patients annually in the United State alone. Based on the agent-based model presented herein, clinicians can predict the sepsis pathogenesis for patients using the prognostic indicators from the simulation results, planning the proper therapeutic interventions accordingly. Impressively, the modeling approach presented creates a friendly user-interface allowing physicians to visualize and capture the potential AIR progression patterns. Based on the computational studies, the simulated behavior of the agent–based model conforms to the mechanisms described by the system dynamics mathematical models established in previous research.


Author(s):  
Naheem Olakunle Adesina ◽  
◽  
Abiodun Alani Ogunseye ◽  
Akindele Opeyemi Areegbe ◽  
Thomas Kokumo Yesufu

2015 ◽  
Vol 12 (12) ◽  
pp. 5765-5777
Author(s):  
Faisal Azam ◽  
Muhammad Sharif ◽  
Sajjad Mohsin

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