Article Commentary: Dealing with Diversity in Computational Cancer Modeling

Johnson, David; McKeever, Steve; Stamatakos, Georgios S.; Dionysiou, Dimitra D.; Graf, Norbert; Sakkalis, Vangelis; Marias, Kostas; Wang, Zhihui; Deisboeck, Thomas S.

doi:10.4137/cin.s11583

Cited by 16 publications

(16 citation statements)

References 59 publications

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“…Innovative computational modeling and simulation, in addition to appropriately designed biological experiments can facilitate a powerful tool to refine highthroughput biological data, hypotheses and more accurate predictions (Macklin and Lowengrub, 2007;Kam et al, 2012;Edelman et al, 2010;Deisboeck et al, 2011;Johnson et al, 2013). These models can be formulated from the concept of biological spatial spaces: atomic, molecular, microscopic, and macroscopic (Anderson et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

An in silico model to demonstrate the effects of Maspin on cancer cell dynamics

Al-Mamun

Farid

Ravenhil

et al. 2016

Journal of Theoretical Biology

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Most cancer treatments efficacy depends on tumor metastasis suppression, where tumor suppressor genes play an important role. Maspin (Mammary Serine Protease Inhibitor), an non-inhibitory serpin has been reported as a potential tumor suppressor to influence cell migration, adhesion, proliferation and apoptosis in in vitro and in vivo experiments in last two decades. Lack of computational investigations hinders its ability to go through clinical trials. Previously, we reported first computational model for maspin effects on tumor growth using artificial neural network and cellular automata paradigm with in vitro data support.This paper extends the previous in silico model by encompassing how maspin influences cell migration and the cell-extracellular matrix interaction in subcellular level. A feedforward neural network was used to define each cell behavior (proliferation, quiescence, apoptosis) which followed a cell-cycle algorithm to show the microenvironment impacts over tumor growth. Furthermore, the model concentrates how the in silico experiments results can further confirm the fact that maspin reduces cell migration using specific in vitro data verification method. The data collected from in vitro and in silico experiments formulates an unsupervised learning problem which can be solved by using different clustering algorithms. A density based clustering technique was developed to measure the similarity between two datasets based on the number of links between instances. Our proposed clustering algorithm first finds the nearest neighbors of each instance, and then redefines the similarity between pairs of instances in terms of how many nearest neighbors share the two instances. The number of links between two instances is defined as the number of common neighbors they have. The results showed significant resemblances with in vitro experimental data. The results also offer a new insight into the dynamics of maspin and establish as a metastasis suppressor gene for further molecular research.

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Section: Related Workmentioning

confidence: 99%

An in silico model to demonstrate the effects of Maspin on cancer cell dynamics

Al-Mamun

Farid

Ravenhil

et al. 2016

Journal of Theoretical Biology

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“…Current computational cancer modeling approaches can be divided into three categories: discrete, continuum, and hybrid, i.e., the combination of both (interested readers should refer to [4-9] for in depth discussions on this topic). Briefly, discrete models employ experimentally-derived, computationally-coded rules to define the step-wise or discrete interactions between individual cells and provide insight on tumor microstructure, cell proliferation and death rates, and cell densities.…”

Section: Introductionmentioning

confidence: 99%

Simulating cancer growth with multiscale agent-based modeling

Wang

Butner

Kerketta

et al. 2015

Seminars in Cancer Biology

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203

147

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There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.

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“…Oncosimulator models also consider multiple scales in aggregate, covering molecular scale interactions and the clinical perspective using patient histories, description of which cannot be integrated into either CellML or SBML models. Further arguments are described in our 2013 Cancer Informatics commentary [10].…”

Section: Rationalementioning

confidence: 99%

“…TumorML true, false also uses a domain-specific taxonomy of models relating to TUMOR that were designed as part of the database schema, summarized in Table 1, where the schema does not strictly enforce the enumerations, but also allows free text strings to be used. A relevant discussion on the different and diverse types of cancer models has been published by the authors in [10]. A list of <reference> elements describe bibliographic references of models and related components of model descriptions to attribute work done elsewhere that the model description is based on.…”

Section: Headermentioning

confidence: 99%

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Connecting digital cancer model repositories with markup

Johnson

McKeever

Deisboeck

et al. 2013

ACM SIGBioinformatics Rec.

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The cancer research community requires a standardized way of describing mathematical and computational models to enable interoperation between systems, repositories, and between the models themselves. In this paper we describe a new markup language, TumorML 1 , for describing computational models that fall within the domain of cancer. TumorML is an XML-based markup language that wraps existing cancer model implementations with metadata for model curation, parametric interface description, implementation description, and compound model linking.In this paper we first introduce the rationale for a new markup language for computational cancer model description based on our experiences and requirements from the European Commission's 'Transatlantic Tumor Model Repositories' project. The aim of the project is to develop a European-based digital cancer model repository to link and interoperate with a similar established repository based in the United States. TumorML was developed to enable this interoperation between repositories. We introduce the language and describe the main features of the specification and go on to describe a real application of TumorML where * Corresponding author. † On leave of absence; current address: ThinkMotu LLC, Wellesley, MA 02481. Email: ts.deisboeck@thinkmotu.com 1 http://www.github.com/tumorml a molecular pathway model has been packaged using the new markup language.

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Article Commentary: Dealing with Diversity in Computational Cancer Modeling

Cited by 16 publications

References 59 publications

An in silico model to demonstrate the effects of Maspin on cancer cell dynamics

An in silico model to demonstrate the effects of Maspin on cancer cell dynamics

Simulating cancer growth with multiscale agent-based modeling

Connecting digital cancer model repositories with markup

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