A Global Method for Simulating Intracellular Signaling Reduces Computational Time In Multiscale Agent-Based Models With Translational Systems Biology Applications

2021 ◽  
Author(s):  
Daniel Bergman ◽  
Randy F. Sweis ◽  
Alexander T. Pearson ◽  
Fereshteh Nazari ◽  
Trachette Jackson
Keyword(s):  
Systems Biology ◽  
Computational Time ◽  
Agent Based Models ◽  
Global Method ◽  
Agent Based

scholarly journals Agent‐based models in translational systems biology

2009 ◽  
Vol 1 (2) ◽  
pp. 159-171 ◽  
Author(s):  
Gary An ◽  
Qi Mi ◽  
Joyeeta Dutta‐Moscato ◽  
Yoram Vodovotz
Keyword(s):  
Systems Biology ◽  
Agent Based Models ◽  
Agent Based

scholarly journals Review of the systems biology of the immune system using agent-based models

2018 ◽  
Vol 12 (3) ◽  
pp. 83-92 ◽  
Author(s):  
Snehal B. Shinde ◽  
Manish P. Kurhekar
Keyword(s):  
Immune System ◽  
Systems Biology ◽  
Agent Based Models ◽  
Agent Based

A Two-level Agent-Based Model for Hurricane Evacuation in New Orleans

2015 ◽  
Vol 12 (2) ◽  
Author(s):  
Wei Liang ◽  
Nina S.-N. Lam ◽  
Xiaojun Qin ◽  
Wenxue Ju
Keyword(s):  
New Orleans ◽  
Urban Areas ◽  
Computational Time ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Hurricane Evacuation ◽  
Agent Based ◽  
The Real ◽  
Evacuation Traffic ◽  
Mass Evacuation

AbstractMass evacuation of urban areas due to hurricanes is a critical problem in emergency management that requires extensive basic and applied research. Previous research uses agent-based models to simulate individual vehicle and driver behavior, and is limited mostly to a small study area due to the complexity of the models and the computational time needed. To better understand evacuation behavior, simulating the evacuation traffic in a larger region is needed. This paper develops a two-level regional disaster evacuation model by coupling two agent-based models. The first model uses each census block centroid, weighted with its corresponding number of vehicles, as an agent to simulate the local road network traffic. The second model, developed on the platform of a commercial software program called VISSIM, treats each vehicle as an agent to simulate the interstate highway traffic. This two-level agent-based model was used to simulate hurricane evacuation traffic in New Orleans. Validation results with the real Hurricane Katrina’s evacuation data confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. The modeling results show that the average root-mean-square error (RMSE) for the three major evacuation directions was 347.58. Under a simultaneous evacuation strategy, and with 240,251 vehicles in 17,744 agents (census blocks), it would take at least 46.3 hours to evacuate all residents from the New Orleans metropolitan area. This two-level modeling approach could serve as a practical tool for evaluating mass evacuation strategies in New Orleans and other similar urban areas.

scholarly journals Validation and Calibration of an Agent-Based Model: A Surrogate Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Zhang ◽  
Zhe Li ◽  
Yongchao Zhang
Keyword(s):  
Surrogate Model ◽  
Original System ◽  
Supervised Machine Learning ◽  
Computational Time ◽  
Chinese Stock Market ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Agent Based ◽  
Asset Pricing Model ◽  
Remarkable Reduction

Agent-based modelling has been proved to be extremely useful for learning about real world societies through the analysis of simulations. Recent agent-based models usually contain a large number of parameters that capture the interactions among microheterogeneous subjects and the multistructure of the complex system. However, this can result in the “curse of dimensionality” phenomenon and decrease the robustness of the model’s output. Hence, it is still a great challenge to efficiently calibrate agent-based models to actual data. In this paper, we present a surrogate analysis method for calibration by combining supervised machine-learning and intelligent iterative sampling. Without any prior assumptions regarding the distribution of the parameter space, the proposed method can learn a surrogate model as the approximation of the original system with a relatively small number of training points, which will serve the needs of further sensitivity analysis and parameter calibration research. We take the heterogeneous asset pricing model as an example to evaluate the model’s performance using actual Chinese stock market data. The results demonstrate the good capabilities of the surrogate model at modelling the observed reality, as well as the remarkable reduction of the computational time for validating the agent-based model.

scholarly journals Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Anna Corti ◽  
Monika Colombo ◽  
Francesco Migliavacca ◽  
Jose Felix Rodriguez Matas ◽  
Stefano Casarin ◽  
...  
Keyword(s):  
Systems Biology ◽  
Computational Modeling ◽  
Cellular Systems ◽  
Emergent Behavior ◽  
Future Research ◽  
Agent Based Models ◽  
Mortality And Morbidity ◽  
Agent Based ◽  
Vascular Adaptation ◽  
Spatial And Temporal Scales

The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

In silico surveillance: highly detailed agent-based models for surveillance system evaluation and design

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Bryan Lewis ◽  
Stephen Eubank ◽  
Allyson Abrams ◽  
Ken Kleinman
Keyword(s):  
In Silico ◽  
Surveillance System ◽  
System Evaluation ◽  
Agent Based Models ◽  
Agent Based ◽  
Surveillance System Evaluation

Agent‐based models to investigate sound impact on marine animals: bridging the gap between effects on individual behaviour and population level consequences

Oikos ◽  
2021 ◽  
Author(s):  
Lars O. Mortensen ◽  
Magda Ewa Chudzinska ◽  
Hans Slabbekoorn ◽  
Frank Thomsen
Keyword(s):  
Population Level ◽  
Agent Based Models ◽  
Marine Animals ◽  
Individual Behaviour ◽  
Agent Based
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