scholarly journals An agent-based model of avascular tumor growth: Immune response tendency to prevent cancer development

SIMULATION ◽  
2017 ◽  
Vol 93 (8) ◽  
pp. 641-657 ◽  
Author(s):  
Fateme Pourhasanzade ◽  
S.H Sabzpoushan ◽  
Ali Mohammad Alizadeh ◽  
Ebrahim Esmati
Keyword(s):  
Immune System ◽  
Tumor Growth ◽  
Tumor Cells ◽  
Immune Cells ◽  
Computational Models ◽  
Growth Fraction ◽  
Agent Based Model ◽  
Agent Based ◽  
Clinical Experiments ◽  
Avascular Tumor

Mathematical and computational models are of great help to study and predict phenomena associated with cancer growth and development. These models may lead to introduce new therapies or improve current treatments by discovering facts that may not be easily discovered in clinical experiments. Here, a new two-dimensional (2D) stochastic agent-based model is presented for the spatiotemporal study of avascular tumor growth based on the effect of the immune system. The simple decision-making rules of updating the states of each agent depend not only on its intrinsic properties but also on its environment. Tumor cells can interact with both normal and immune cells in their Moore neighborhood. The effect of hypoxia has been checked off by considering non-mutant proliferative tumor cells beside mutant ones. The recruitment of immune cells after facing a mass of tumor is also considered. Results of the simulations are presented before and after the appearance of immune cells in the studied tissue. The growth fraction and necrotic fraction are used as output parameters along with a 2D graphical growth presentation. Finally, the effect of input parameters on the output parameters generated by the model is discussed. The model is then validated by an in vivo study published in medical articles. The results show a multi-spherical tumor growth before the immune system strongly involved in competition with tumor cells. Besides, considering the immune system in the model shows more compatibility with biological facts. The effect of the microenvironment on the proliferation of cancer and immune cells is also studied.

scholarly journals An Agent-Based Model of Avascular Tumor Growth

2015 ◽  
Author(s):  
Ana Victoria Ponce Bobadilla ◽  
◽  
René Doursat ◽  
François Amblard
Keyword(s):  
Tumor Growth ◽  
Agent Based Model ◽  
Agent Based ◽  
Avascular Tumor

scholarly journals On the modeling of the interaction between tumor growth and the immune system using some new fractional and fractional-fractal operators

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Behzad Ghanbari
Keyword(s):  
Immune System ◽  
Infectious Diseases ◽  
Tumor Growth ◽  
Computational Models ◽  
Recent Literature ◽  
Approximate Solutions ◽  
Biological Phenomena ◽  
Uniqueness Of The Solution ◽  
Biological Concepts ◽  
New Research

Abstract Humans are always exposed to the threat of infectious diseases. It has been proven that there is a direct link between the strength or weakness of the immune system and the spread of infectious diseases such as tuberculosis, hepatitis, AIDS, and Covid-19 as soon as the immune system has no the power to fight infections and infectious diseases. Moreover, it has been proven that mathematical modeling is a great tool to accurately describe complex biological phenomena. In the recent literature, we can easily find that these effective tools provide important contributions to our understanding and analysis of such problems such as tumor growth. This is indeed one of the main reasons for the need to study computational models of how the immune system interacts with other factors involved. To this end, in this paper, we present some new approximate solutions to a computational formulation that models the interaction between tumor growth and the immune system with several fractional and fractal operators. The operators used in this model are the Liouville–Caputo, Caputo–Fabrizio, and Atangana–Baleanu–Caputo in both fractional and fractal-fractional senses. The existence and uniqueness of the solution in each of these cases is also verified. To complete our analysis, we include numerous numerical simulations to show the behavior of tumors. These diagrams help us explain mathematical results and better describe related biological concepts. In many cases the approximate results obtained have a chaotic structure, which justifies the complexity of unpredictable and uncontrollable behavior of cancerous tumors. As a result, the newly implemented operators certainly open new research windows in further computational models arising in the modeling of different diseases. It is confirmed that similar problems in the field can be also be modeled by the approaches employed in this paper.

scholarly journals A 3D Agent-Based Model of Lung Fibrosis

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 90
Author(s):  
Nicolò Cogno ◽  
Roman Bauer ◽  
Marco Durante
Keyword(s):  
Lung Fibrosis ◽  
Computational Models ◽  
Multiple Scales ◽  
Inflammatory Diseases ◽  
Cell Damage ◽  
Heterogeneous Systems ◽  
Chronic Obstructive ◽  
Agent Based Model ◽  
Agent Based ◽  
Radiation Induced

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).

An enhanced agent based model of the immune system response

2006 ◽  
Vol 244 (2) ◽  
pp. 77-79 ◽  
Author(s):  
V. Baldazzi ◽  
F. Castiglione ◽  
M. Bernaschi
Keyword(s):  
Immune System ◽  
System Response ◽  
Agent Based Model ◽  
Agent Based ◽  
Immune System Response

scholarly journals Strategies to Interfere with Tumor Metabolism through the Interplay of Innate and Adaptive Immunity

Cells ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 445 ◽  
Author(s):  
Javier Mora ◽  
Christina Mertens ◽  
Julia K. Meier ◽  
Dominik C. Fuhrmann ◽  
Bernhard Brüne ◽  
...  
Keyword(s):  
Tumor Growth ◽  
Tumor Cells ◽  
Immune Cells ◽  
Tumor Development ◽  
Metabolic Activation ◽  
Growth And Survival ◽  
Metabolic Characteristics ◽  
Inflammatory Phenotype ◽  
Tumor Outgrowth

The inflammatory tumor microenvironment is an important regulator of carcinogenesis. Tumor-infiltrating immune cells promote each step of tumor development, exerting crucial functions from initiation, early neovascularization, to metastasis. During tumor outgrowth, tumor-associated immune cells, including myeloid cells and lymphocytes, acquire a tumor-supportive, anti-inflammatory phenotype due to their interaction with tumor cells. Microenvironmental cues such as inflammation and hypoxia are mainly responsible for creating a tumor-supportive niche. Moreover, it is becoming apparent that the availability of iron within the tumor not only affects tumor growth and survival, but also the polarization of infiltrating immune cells. The interaction of tumor cells and infiltrating immune cells is multifaceted and complex, finally leading to different activation phenotypes of infiltrating immune cells regarding their functional heterogeneity and plasticity. In recent years, it was discovered that these phenotypes are mainly implicated in defining tumor outcome. Here, we discuss the role of the metabolic activation of both tumor cells and infiltrating immune cells in order to adapt their metabolism during tumor growth. Additionally, we address the role of iron availability and the hypoxic conditioning of the tumor with regard to tumor growth and we describe the relevance of therapeutic strategies to target such metabolic characteristics.

Agent-Based Model of the Endocrine Pancreas and Interaction with Innate Immune System

2010 ◽  
pp. 157-164
Author(s):  
Ignacio V. Martínez Espinosa ◽  
Enrique J. Gómez Aguilera ◽  
María E. Hernando Pérez ◽  
Ricardo Villares ◽  
José Mario Mellado García
Keyword(s):  
Immune System ◽  
Innate Immune System ◽  
Endocrine Pancreas ◽  
Innate Immune ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Combinations of TLR Ligands: A Promising Approach in Cancer Immunotherapy

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Saskia Stier ◽  
Claudia Maletzki ◽  
Ulrike Klier ◽  
Michael Linnebacher
Keyword(s):  
Tumor Growth ◽  
Tumor Cells ◽  
Cell Lines ◽  
Immune Cells ◽  
Human Lymphocytes ◽  
Growth Control ◽  
Poly I:C ◽  
The Impact

Toll-like receptors (TLRs), a family of pattern recognition receptors recognizing molecules expressed by pathogens, are typically expressed by immune cells. However, several recent studies revealed functional TLR expression also on tumor cells. Their expression is a two-sided coin for tumor cells. Not only tumor-promoting effects of TLR ligands are described but also direct oncopathic and immunostimulatory effects. To clarify TLRs’ role in colorectal cancer (CRC), we tested the impact of the TLR ligands LPS, Poly I:C, R848, and Taxol on primary human CRC cell lines (HROC40, HROC60, and HROC69)in vitroandin vivo(CT26). Taxol, not only a potent tumor-apoptosis-inducing, but also TLR4-activating chemotherapeutic compound, inhibited growth and viability of all cell lines, whereas the remaining TLR ligands had only marginal effects (R848 > LPS > Poly I:C). Combinations of the substances here did not improve the results, whereas antitumoral effects were dramatically boosted when human lymphocytes were added. Here, combining the TLR ligands often diminished antitumoral effects.In vivo, best tumor growth control was achieved by the combination of Taxol and R848. However, when combined with LPS, Taxol accelerated tumor growth. These data generally prove the potential of TLR ligands to control tumor growth and activate immune cells, but they also demonstrate the importance of choosing the right combinations.

A multiscale agent-based framework integrated with a constraint-based metabolic network model of cancer for simulating avascular tumor growth

2017 ◽  
Vol 13 (9) ◽  
pp. 1888-1897 ◽  
Author(s):  
Mehrdad Ghadiri ◽  
Mahshid Heidari ◽  
Sayed-Amir Marashi ◽  
Seyed Hasan Mousavi
Keyword(s):  
Tumor Growth ◽  
Metabolic Network ◽  
Network Model ◽  
Agent Based ◽  
Metabolic Network Model ◽  
Avascular Tumor

The integration of an agent-based framework with a constraint-based metabolic network model of cancer for simulating avascular tumor growth.

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