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

Galvão, Viviane; Miranda, José García Vivas

doi:10.1016/j.biosystems.2010.03.007

Cited by 10 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Activated myofibroblasts have been observed in human cardiac tissue many years after an infarct, indicating the potential for long-term tissue remodeling by fibroblasts [8]. Fibroblast activation can be triggered by many other forms of cardiac injury including pressure overload [1], diabetes [9], and infectious diseases such as Chagas disease[10] or Coxsackie B3 infection [11]. …”

Section: Introductionmentioning

confidence: 99%

“…Galvao and colleagues have developed 2D and 3D ABMs of Chaga’s cardiomyopathy by including not just fibroblasts and fibrosis but also the Trypanosoma cruzi parasites, inflammatory cells, cardiomyocytes, bone marrow stem cells (BMSC), and tumor necrosis factor-α [10,71]. In their lattice-based simulations, each grid location is designated as one of these component types or left empty, and successive time steps updates each location designation according to simple, experimentally-motivated rules based on which component types are nearby.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational modeling of cardiac fibroblasts and fibrosis

Zeigler

Richardson

Holmes

et al. 2016

Journal of Molecular and Cellular Cardiology

View full text Add to dashboard Cite

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational modeling of cardiac fibroblasts and fibrosis

Zeigler

Richardson

Holmes

et al. 2016

Journal of Molecular and Cellular Cardiology

View full text Add to dashboard Cite

show abstract

“…Recently, some computer models have been used to gain insight into a variety of diseases caused by protozoan. Examples includes Chagas disease [5][6][7]9], malaria [4], toxoplasmosis [14], and leishmaniasis [1,2,11]. Several approaches used to describe the disease evolution on a cellular level are based on differential equation, cellular automata and multi-agents.…”

Section: Introductionmentioning

confidence: 99%

Modeling of leishmaniasis infection by using multi-agents systems

Galvão¹,

Miranda²,

Andrade³

2016

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

Self Cite

View full text Add to dashboard Cite

Abstract. The World Health Organization considers leishmaniasis as one of the six most important tropical diseases in the world. Actually, it is endemic in 88 countries and the different types of this disease affects approximately 12 million of people worldwide. Leishmaniasis is caused by protozoan parasites of the genus Leishmania. The mechanisms of the cellular response against leishmaniasis are not completely known. Additionally, there is still a lack of understanding on the metabolism of the parasite. In this way, we have developed and analyzed a computational model for the immune response to Leishmania major infection by using multi-agent systems. In our model, we have seven different cell types: Leishmania major, CD4-T cell (resting and activated), macrophage (resting, activated, infected, chronically infected), eosinophil (resting and activated), neutrophil, dendritic cell (resting and activated), and keratinocyte. Furthermore, there are void sites to simulate the mobility of the cells. Our model can simulate migration, activation, phagocytosis, and cellular death by lifetime. It was constructed on a three-dimensional cubic lattice. The results show that the kinetics of different cell types does not change for different lattice size. Furthermore, our results suggest that an agent-based approach is a suitable instrument for investigating the cellular interaction. Keywords.multi-agent, leishmaniasis, computational model, immune response, protozoan infection 1 vivgalvao@gmail.com 2

show abstract

“…The traditional and one of the most acceptable techniques is to choose an arbitrary large number, e.g. 10 [320], 20 [184] and 1000 [321], and assume it is an adequate "number" to address the issue. Although the average value of multiple runs is more accurate than a single run, it is not possible to check whether the number of runs is large enough or not.…”

Section: Number Of Simulation Runsmentioning

confidence: 99%

“…Moore neighbourhood was used to understand Chagus disease evolution [184], tumour development [185] and the cracking process of rock [192]. Margolus neighbourhood has already been used to simulate deformation in clays [23,193] and diffusion and fluid flow in porous media [194].…”

Section: Agent Statesmentioning

confidence: 99%

An Innovative Agent-Based Cellular Automata Framework for Simulating Articular Cartilage Biomechanics

Kashani¹

View full text Add to dashboard Cite

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

Cited by 10 publications

References 31 publications

Computational modeling of cardiac fibroblasts and fibrosis

Computational modeling of cardiac fibroblasts and fibrosis

Modeling of leishmaniasis infection by using multi-agents systems

An Innovative Agent-Based Cellular Automata Framework for Simulating Articular Cartilage Biomechanics

Contact Info

Product

Resources

About