A model of stem cell population dynamics: in silico analysis and in vivo validation

Setty, Yaki; Dalfó, Diana; Korta, Dorota Z.; Hubbard, E. Jane Albert; Kugler, Hillel

doi:10.1242/dev.067512

Cited by 19 publications

(31 citation statements)

References 49 publications

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“…Whole C. elegans germ line modeling, I. A : A lattice‐based model in which each germ cell occupies a certain number of squares according to its differentiation status (modified and reproduced, with permission, from Setty et al, ). Germ cells regularly move to adjacent, unoccupied squares.…”

Section: Testing Hypotheses In Context and Generating New Predictionsmentioning

confidence: 99%

“…A labeled photomicrograph of a C. elegans gonad arm is shown alongside for comparison. B : Part of the statechart governing germ cell behavior in the model shown in “A” (modified and reproduced, with permission, from Setty et al, ). Included regions controlling the cell cycle, differentiation, GLP‐1 receptor state, and movement direction.…”

Section: Testing Hypotheses In Context and Generating New Predictionsmentioning

confidence: 99%

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How computational models contribute to our understanding of the germ line

Atwell

Dunn

Osborne

et al. 2016

Molecular Reproduction Devel

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SUMMARYComputational models are an invaluable tool in modern biology. They provide a framework within which to summarize existing knowledge, enable competing hypotheses to be compared qualitatively and quantitatively, and to facilitate the interpretation of complex data. Moreover, models allow questions to be investigated that are difficult to approach experimentally. Theories can be tested in context, identifying the gaps in our understanding and potentially leading to new hypotheses. Models can be developed on a variety of scales and with different levels of mechanistic detail, depending on the available data, the biological questions of interest, and the available mathematical and computational tools. The goal of this review is to provide a broad picture of how modeling has been applied to reproductive biology. Specifically, we look at four uses of modeling: (i) comparing hypotheses; (ii) interpreting data; (iii) exploring experimentally challenging questions; and (iv) hypothesis evaluation and generation. We present examples of each of these applications in reproductive biology, drawing from a range of organismsÀ Àincluding Drosophila, Caenorhabditis elegans, mouse, and humans. We aim to describe the data and techniques used to construct each model, and to highlight the benefits of modeling to the field, as complementary to experimental work.Mol. Reprod. Dev. 83: 944À957, 2016. ß 2016 Wiley Periodicals, Inc. [T]he process of developing and`r unning'' a formal model requires that the assumptions underlying specific hypotheses are made explicit, and exposes potential gaps in current knowledge.

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Section: Testing Hypotheses In Context and Generating New Predictionsmentioning

confidence: 99%

Section: Testing Hypotheses In Context and Generating New Predictionsmentioning

confidence: 99%

How computational models contribute to our understanding of the germ line

Atwell

Dunn

Osborne

et al. 2016

Molecular Reproduction Devel

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“…The model captured the general developmental progression of germline development as a dynamic process over time [45] (Figure 3A, Top). We first altered the model design in ways that mimic known mutants.…”

Section: Reviewmentioning

confidence: 99%

In-silico models of stem cell and developmental systems

Setty

2014

Theor Biol Med Model

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Understanding how developmental systems evolve over time is a key question in stem cell and developmental biology research. However, due to hurdles of existing experimental techniques, our understanding of these systems as a whole remains partial and coarse. In recent years, we have been constructing in-silico models that synthesize experimental knowledge using software engineering tools. Our approach integrates known isolated mechanisms with simplified assumptions where the knowledge is limited. This has proven to be a powerful, yet underutilized, tool to analyze the developmental process. The models provide a means to study development in-silico by altering the model’s specifications, and thereby predict unforeseen phenomena to guide future experimental trials. To date, three organs from diverse evolutionary organisms have been modeled: the mouse pancreas, the C. elegans gonad, and partial rodent brain development. Analysis and execution of the models recapitulated the development of the organs, anticipated known experimental results and gave rise to novel testable predictions. Some of these results had already been validated experimentally. In this paper, I review our efforts in realistic in-silico modeling of stem cell research and developmental biology and discuss achievements and challenges. I envision that in the future, in-silico models as presented in this paper would become a common and useful technique for research in developmental biology and related research fields, particularly regenerative medicine, tissue engineering and cancer therapeutics.

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“…Conventional cellular and molecular biological techniques are limited in their ability to explain complex biological phenomenon, and thus computational approaches have been introduced as a means to model branching morphogenesis [21]. Computational modeling of morphogenesis dates back to the mid 20th century with important mathematical models that advanced our understanding of fundamental properties of clusters of cells [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Growth Factor-Dependent Cleft Formation During Branching Morphogenesis Using A Dynamic Graph-Based Growth Model

Dhulekar

Ray

Yuan

et al. 2016

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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This study considers the problem of describing and predicting cleft formation during the early stages of branching morphogenesis in mouse submandibular salivary glands (SMG) under the influence of varied concentrations of epidermal growth factors (EGF). Given a time-lapse video of a growing SMG, first we build a descriptive model that captures the underlying biological process and quantifies the ground truth. Tissue-scale (global) and morphological features related to regions of interest (local features) are used to characterize the biological ground truth. Second, we devise a predictive growth model that simulates EGF-modulated branching morphogenesis using a dynamic graph algorithm, which is driven by biological parameters such as EGF concentration, mitosis rate, and cleft progression rate. Given the initial configuration of the SMG, the evolution of the dynamic graph predicts the cleft formation, while maintaining the local structural characteristics of the SMG. We determined that higher EGF concentrations cause the formation of higher number of buds and comparatively shallow cleft depths. Third, we compared the prediction accuracy of our model to the Glazier-Graner-Hogeweg (GGH) model, an on-lattice Monte-Carlo simulation model, under a specific energy function parameter set that allows new rounds of de novo cleft formation. The results demonstrate that the dynamic graph model yields comparable simulations of gland growth to that of the GGH model with a significantly lower computational complexity. Fourth, we enhanced this model to predict the SMG morphology for an EGF concentration without the assistance of a ground truth time-lapse biological video data; this is a substantial benefit of our model over other similar models that are guided and terminated by information regarding the final SMG morphology. Hence, our model is suitable for testing the impact of different biological parameters involved with the process of branching morphogenesis in silico, while reducing the requirement of in vivo experiments.

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A model of stem cell population dynamics: in silico analysis and in vivo validation

Cited by 19 publications

References 49 publications

How computational models contribute to our understanding of the germ line

How computational models contribute to our understanding of the germ line

In-silico models of stem cell and developmental systems

Prediction of Growth Factor-Dependent Cleft Formation During Branching Morphogenesis Using A Dynamic Graph-Based Growth Model

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