A Boolean network model of human gonadal sex determination

Osiris, Rios; Frias, Sara; Rodríguez, Alfredo; Kofman, Susana; Merchant, Hugo; Torres, Leda; Mendoza, Luis

doi:10.1186/s12976-015-0023-0

Cited by 46 publications

(46 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A benefit of using these Boolean models is that they could serve as the ground-truth regulatory networks to evaluate the performance of proposed GRNs. We collected four Boolean models for Mammalian Cortical Area Development (mCAD) [28], Ventral Spinal Cord Development (VSC) [29], Hematopoietic Stem Cell Differentiation (HSC) [30], and Gonadal Sex Determination (GSD) [31]. Figure 4(a) displays these networks.…”

Section: Datasets From Curated Modelsmentioning

confidence: 99%

“…Gonadal Sex Determination. Rios et al 2015 modeled the gonadal differentiation circuit that regulates the maturation of the Bipotential Gonadal Primordium (BGP) into either male (testes) or female (ovary) gonads [31]. The model consists of 18 genes and a node representing the Urogenital Ridge (UGR), which serves as the input to the model.…”

Section: Hematopoietic Stem Cell Differentiationmentioning

confidence: 99%

See 1 more Smart Citation

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Pratapa

Jalihal

Law

et al. 2019

Preprint

133

285

View full text Add to dashboard Cite

We present a comprehensive evaluation of state-of-the-art algorithms for inferring gene regulatory networks (GRNs) from single-cell gene expression data. Our contributions include a comprehensive evaluation pipeline based on simulated data from "toy", artificial networks with predictable cellular trajectories and on simulated data from carefully-curated Boolean models. We develop a strategy to simulate these two types of data that avoids the pitfalls of existing strategies that have been used to mimic bulk transcriptional data. We found that the accuracy of the algorithms measured in terms of AUROC and AUPRC was moderate, by and large, although the methods were better in recovering interactions in the artificial networks than the Boolean models. Techniques that did not require pseudotime-ordered cells were more accurate, in general. There were an excess of feedforward loops in predicted networks than in the Boolean models. The observation that the endpoints of many false positive edges were connected by paths of length two in the Boolean models suggested that indirect effects may be predominant in the outputs of these algorithms. The outputs of the methods were quite inconsistent with each other, indicating that combining these approaches using ensembles is likely to be challenging. We present recommendations on how to create simulated gene expression datasets for testing GRN inference algorithms. We suggest that new ideas for avoiding the prediction of indirect interactions appear to be necessary to improve the accuracy of GRN inference algorithms for single cell gene expression data. Simulated data from synthetic networks Simulated data from curated models LEAP PIDC GRN inference methods Predicted networks SCODE LEAP PIDC SCODE Run algorithms Parameter search Software run time Evaluate network motifs ROC Early Precision Stability of inferred networks

show abstract

Section: Datasets From Curated Modelsmentioning

confidence: 99%

Section: Hematopoietic Stem Cell Differentiationmentioning

confidence: 99%

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Pratapa

Jalihal

Law

et al. 2019

Preprint

133

285

View full text Add to dashboard Cite

show abstract

“…In vitro and animal studies observed an impaired gene expression of steroidogenic enzymes and hormones associated with male reproductive health after the exposure to phthalates [ 245 , 246 ]. Besides, the phthalates modification of gene expression can also influence the onset of cancer.…”

Section: Intracellular Signaling Mechanisms Of Phthalates’ Action mentioning

confidence: 99%

Effects and Mechanisms of Phthalates’ Action on Reproductive Processes and Reproductive Health: A Literature Review

Hlisníková

Petrovičová

Kolena

et al. 2020

IJERPH

151

View full text Add to dashboard Cite

The production of plastic products, which requires phthalate plasticizers, has resulted in the problems for human health, especially that of reproductive health. Phthalate exposure can induce reproductive disorders at various regulatory levels. The aim of this review was to compile the evidence concerning the association between phthalates and reproductive diseases, phthalates-induced reproductive disorders, and their possible endocrine and intracellular mechanisms. Phthalates may induce alterations in puberty, the development of testicular dysgenesis syndrome, cancer, and fertility disorders in both males and females. At the hormonal level, phthalates can modify the release of hypothalamic, pituitary, and peripheral hormones. At the intracellular level, phthalates can interfere with nuclear receptors, membrane receptors, intracellular signaling pathways, and modulate gene expression associated with reproduction. To understand and to treat the adverse effects of phthalates on human health, it is essential to expand the current knowledge concerning their mechanism of action in the organism.

show abstract

“…This is truly surprising as it does not require any kinetic constants, as was first noted by Albert and Othmer (2003b). Today, examples of such models range from cell cycle control networks, developmental circuits, to apoptosis and cancer networks, among others (Mendoza et al 1999;Thieffry 2001,2003;Albert and Othmer 2003;Espinosa-Soto 2004;Li et al 2004;Zhang 2008;Bornholdt 2008a, 2013;Faure and Thieffry 2009;Krumsiek et al 2011;Lovrics et al 2014;Zañudo and Albert 2015;Ríos et al 2015;Zhou et al 2016b;Arias Del Angel et al 2018;Yachie-Kinoshita et al 2018;Zañudo et al 2018). Even a tinker toy set for simulating small genetic networks from biology with hardware Boolean networks is available today (Bornholdt and Kopperschmidt 2015).…”

Section: Introductionmentioning

confidence: 99%

Ensembles, dynamics, and cell types: Revisiting the statistical mechanics perspective on cellular regulation

Bornholdt

Kauffman

2019

Journal of Theoretical Biology

View full text Add to dashboard Cite

Genetic regulatory networks control ontogeny. For fifty years Boolean networks have served as models of such systems, ranging from ensembles of random Boolean networks as models for generic properties of gene regulation to working dynamical models of a growing number of sub-networks of real cells. At the same time, their statistical mechanics has been thoroughly studied. Here we recapitulate their original motivation in the context of current theoretical and empirical research. We discuss ensembles of random Boolean networks whose dynamical attractors model cell types. A sub-ensemble is the critical ensemble. There is now strong evidence that genetic regulatory networks are dynamically critical, and that evolution is exploring the critical sub-ensemble. The generic properties of this subensemble predict essential features of cell differentiation. In particular, the number of attractors in such networks scales as the DNA content raised to the 0.63 power. Data on the number of cell types as a function of the DNA content per cell shows a scaling relationship of 0.88. Thus, the theory correctly predicts a power law relationship between the number of cell types and the DNA contents per cell, and a comparable slope. We discuss these new scaling values and show prospects for new research lines for Boolean networks as a base model for systems biology.* bornholdt@uni-bremen.de

show abstract

A Boolean network model of human gonadal sex determination

Cited by 46 publications

References 55 publications

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Effects and Mechanisms of Phthalates’ Action on Reproductive Processes and Reproductive Health: A Literature Review

Ensembles, dynamics, and cell types: Revisiting the statistical mechanics perspective on cellular regulation

Contact Info

Product

Resources

About