Studying the capability of different cancer hallmarks to initiate tumor growth using a cellular automaton simulation. Application in a cancer stem cell context

Monteagudo, Ángel; Santos, José Victor de Oliveira

doi:10.1016/j.biosystems.2013.11.001

Cited by 20 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We followed the event model used by Abbott et al [1] in their study of the likely sequences of precancerous mutations that end in cancer, to simulate the behavior of cells when different hallmarks are acquired, as previously exposed in more detail in our previous works [20] [27].…”

Section: Event Model For Tumor Growth Simulationmentioning

confidence: 99%

“…Basanta et al [3] used a CA model based on the Hanahan and Weinberg hallmarks, focusing their work on analyzing the effect of different environmental conditions on the sequence of acquisition of phenotypic traits. In previous works we also used CA to model the behavior of cells when the hallmarks are present and in the avascular phase, with a different aim to those previous works, since they focused on the study of the multicellular system dynamics in terms of emergent behaviors that can be obtained, analyzing the relative importance of different hallmarks [26] [27] and the capability of Cancer Stem Cells (CSCs) and hallmarks to generate tumor growth and regrowth in different conditions [19] [20].…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context

Monteagudo

Santos

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

We used evolutionary computing for optimizing cancer treatments taking into account the presence and effects of cancer stem cells. We used a cellular automaton to model tumor growth at cellular level, based on the presence of the main cancer hallmarks in the cells. The cellular automaton allows the study of the emergent behavior of the multicellular system evolution in different scenarios defined by the predominance of the different hallmarks. When cancer stem cells (CSCs) are modeled, the multicellular system evolution is additionally dependent on the CSC tumor regrowth capability because their differentiation to non-stem cancer cells. When a standard treatment is applied against nonstem (differentiated) cancer cells, different effects are present depending on the strategy used to eliminate these non-stem cancer cells. We used Differential Evolution to optimize the treatment application strategy in terms of intensity, duration and periodicity to minimize the final outcome of tumor growth and regrowth.

show abstract

Section: Event Model For Tumor Growth Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context

Monteagudo

Santos

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Monte Carlo approaches are used for simulation of system evolution, including statistical predictions of variability. Both approaches have been used to study cell or organism interaction and spatial or morphological pattern behavior, especially for tumor growth, immune cell interactions, and infectious agent dynamics …”

Section: Stage 3 Representing the Biology: Developing The Model Strumentioning

confidence: 99%

“…Both approaches have been used to study cell or organism interaction and spatial or morphological pattern behavior, espe-cially for tumor growth, immune cell interactions, and infectious agent dynamics. 60,[68][69][70] Hybrid and integrated models. Models in which different formalisms are integrated are valuable when the requirements for capturing spatial and dynamic behaviors differ among submodules.…”

Section: Mathematical Modeling Formalismsmentioning

confidence: 99%

A Six‐Stage Workflow for Robust Application of Systems Pharmacology

Gadkar¹,

Kirouac²,

Mager

et al. 2016

CPT Pharmacom & Syst Pharma

107

View full text Add to dashboard Cite

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

show abstract

“…On the other hand, as a survey of these publications also demonstrates, the diversity of topics is remarkable, thereby giving testimony to the versatility of CA modeling as a powerful tool to better understand the theory of biological problems. Biological phenomena that have been modeled as CA include brain oscillations and neural network activities in neuroscience (Traub et al , ; ; Lewis and Rinzel, ; Kozma and Puljic, ; Matsubara and Torikai, ); heart rhythms in cardiac physiology (Bardou et al , ; Barbosa, ; Sabzpoushan and Pourhasanzade, ; Makowiec et al , ); host–pathogen interactions in microbiology and virology (Agur, ; Bru and Cardona, ; Wcisło et al , ; Sinha et al , ); epidemics caused by viruses (Zorzenon dos Santos and Coutinho, ; Beauchemin et al , ; Xiao et al , ; White et al , ); interspecific competition, habitat invasion, and land use in ecology and environmental sciences (Aurambout et al , ; Grimm et al , ; Johnston and Purkis, ; ; Kalmykov and Kalmykov, ; Qiang and Lam, ); and behavior of tumor cells in cancer biology (Deroulers et al , ; DuBois et al , ; Chen et al , ; Monteagudo and Santos, ; Tzedakis et al , ; Dufour et al , ).…”

Section: Introductionmentioning

confidence: 99%

Cellular Automata Modeling of Stem‐Cell‐Driven Development of Tissue in the Nervous System

Lehotzky

Zupanc

2019

Developmental Neurobiology

View full text Add to dashboard Cite

Mathematical and computationalmodeling enables biologists to integrate data from observations and experiments into a theoretical frame work. In this review, we describe how developmental processes associated with stemcelldriven growth of tissue in both the embryonic and adult nervous system can be modeled using cellular automata (CA). A cellular automaton is defined by its discrete nature in time, space, and state. The discrete space is represented by a uniform grid or lattice containing agents that interact with other agents within their local neighborhood. This possibility of local interactions of agents makes the cellular automata approach particularly well suited for studying through modeling how complex patterns at the tissue level emerge from fundamental developmental processes (such as proliferation, migration, differentiation, and death) at the singlecell level. As part of this review, we provide a primer for how to define biologically inspired rules governing these processes so that they can be implemented into a CA model. We then demonstrate the power of the CA approach by presenting simulations (in the form of figures and movies) based on building models of three developmental systems: the formation of the enteric nervous system through invasion by neural crest cells; the growth of normal and tumorous neurospheres induced by proliferation of adult neural stem/progenitor cells; and the neural fate specification through lateral inhibition of embryonic stem cells in the neurogenic region of Drosophila.

show abstract

Studying the capability of different cancer hallmarks to initiate tumor growth using a cellular automaton simulation. Application in a cancer stem cell context

Cited by 20 publications

References 24 publications

Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context

Evolutionary Optimization of Cancer Treatments in a Cancer Stem Cell Context

A Six‐Stage Workflow for Robust Application of Systems Pharmacology

Cellular Automata Modeling of Stem‐Cell‐Driven Development of Tissue in the Nervous System

Contact Info

Product

Resources

About