The recent advances in the mathematical modelling of human pluripotent stem cells

Wadkin, Laura E; Orozco-Fuentes, Sirio; Neganova, Irina; Lako, Majlinda; Shukurov, Anvar; Parker, N. G.

doi:10.1007/s42452-020-2070-3

Cited by 16 publications

(19 citation statements)

References 118 publications

(238 reference statements)

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“…ABM provides a multiscale investigation of a system as a direct observation can be made on individual cells while the cumulative results are captured at the population level [19] , [20] , [21] . ABM has been widely used in the literature to study cellular responses [15] , [17] , [18] . A common challenge in ABM is the abstraction of cellular behavior which requires an algorithm to correctly govern the decision-making process [22] , [23] .…”

Section: Introductionmentioning

confidence: 99%

“…Such an algorithm receives cellular inputs at the microscale and predicts cellular behavior. Several approaches have been proposed in the literature to simulate the decision-making process such as simple rule definition, differential equations, logic-based approach, and artificial neural networks [15] , [24] , [25] , [26] . Fuzzy logic (FL)-based have shown great potential in resolving technical barriers between experimental and simulation experts thanks to its plain language [27] .…”

Section: Introductionmentioning

confidence: 99%

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Magnesium ions regulate mesenchymal stem cells population and osteogenic differentiation: A fuzzy agent-based modeling approach

Nourisa

Zeller‐Plumhoff

Helmholz

et al. 2021

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Magnesium ions regulate mesenchymal stem cells population and osteogenic differentiation: A fuzzy agent-based modeling approach

Nourisa

Zeller‐Plumhoff

Helmholz

et al. 2021

Computational and Structural Biotechnology Journal

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show abstract

“…For over a last few decades, various studies indicated the pertinence of miRNAs biology in cancer, showing that they can behave both as oncogenes and tumor repressors; negatively regulating the protein-coding oncogenes [6,7]. Many researchers have dictated that miRNAs can influence cancer phenotypes and many reports have exhibited miR-NAs expression profiles which provide detail about tumor origin, prognosis and diagnosis of cancer [8].…”

Section: Microrna Discoverymentioning

confidence: 99%

Circulating microRNAs in oncogenic viral infections: potential diagnostic biomarkers

2020

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Cancer is a leading cause of high death rate worldwide. One strategy to control the disease is the early diagnosis by novel biomarkers that express during early stage of the disease. The recent diagnostic strategies in cancer don't have enough specificity to promote the detection of cancer at its beginning. Many biomarkers like protein biomarkers and metabolites are being used for diagnosis of various cancer types but miRNAs are excellent among them, because they have distinctive biochemical characteristics. Moreover, to raise the precision and capability of miRNA to diagnose cancer, the analyzing of both miRNAs and as well as selective mRNA will help in creating a more complete categorizer. Virus constitutes the cause of 20% of entire human cancer cases and both RNA and DNA viruses are linked with tumors in both animal and man. Even though many viruses can cause different tumors in animals, only some of them are linked with human cancers and are presently regarded as oncogenic viruses. These viruses include Human Papillomavirus (HPV), Hepatitis B (HBV) and Hepatitis C Virus (HCV), Epstein Barr Virus (EBV), Human Herpes Virus 8 (HHV8), Human T cell Leukemia Virus (HTLV) and Merkel Cell Polyomavirus (MCPyV). Expression data of miRNA in several cancers reveal that miRNA profile is different in cancer cells as compared to normal cells. So, miRNA could be useful biomarker for the detection of cancer. The present study strengthens a foundation and gives a logic to investigate the ability of miRNAs as circulating biomarkers in various cancers.

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“…However, careful, experiment-based quantification of the stochastic, temporal dynamics of PTFs is necessary to examine the resulting effects on cell fate. Statistical analysis and mathematical modelling are deepening our understanding of hPSC behaviours and guiding the development of experimental protocols [21]. Recent mathematical models of cell pluripotency focus on describing the network of PTFs and the resulting cell fate decisions to guide the optimisation and control of pluripotency February 26, 2021 2/37 in-vitro [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A mathematical modelling framework for the regulation of intra-cellular OCT4 in human pluripotent stem cells

Wadkin

Orozco-Fuentes

Neganova

et al. 2021

Preprint

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Human pluripotent stem cells (hPSCs) have promising clinical applications in regenerative medicine, drug-discovery and personalised medicine due to their potential to differentiate into all cell types, a property know as pluripotency. A deeper understanding of how pluripotency is regulated is required to assist in controlling pluripotency and differentiation trajectories experimentally. Mathematical modelling provides a non-invasive tool through which to explore, characterise and replicate the regulation of pluripotency and the consequences on cell fate. Here we use experimental data of the expression of the pluripotency transcription factor OCT4 in a growing hPSC colony to develop and evaluate mathematical models for temporal pluripotency regulation. We consider fractional Brownian motion and the stochastic logistic equation and explore the effects of both additive and multiplicative noise. We illustrate the use of time-dependent carrying capacities and the introduction of Allee effects to the stochastic logistic equation to describe cell differentiation. This mathematical framework for describing intra-cellular OCT4 regulation can be extended to other transcription factors and developed into sophisticated predictive models.

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The recent advances in the mathematical modelling of human pluripotent stem cells

Cited by 16 publications

References 118 publications

Magnesium ions regulate mesenchymal stem cells population and osteogenic differentiation: A fuzzy agent-based modeling approach

Magnesium ions regulate mesenchymal stem cells population and osteogenic differentiation: A fuzzy agent-based modeling approach

Circulating microRNAs in oncogenic viral infections: potential diagnostic biomarkers

A mathematical modelling framework for the regulation of intra-cellular OCT4 in human pluripotent stem cells

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