scholarly journals Distinguishing between models of mammalian gene expression: telegraph-like models versus mechanistic models

2021 ◽  
Author(s):  
Svitlana Braichenko ◽  
James Holehouse ◽  
Ramon Grima
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Mammalian Cells ◽  
Mechanistic Model ◽  
Waiting Times ◽  
Mechanistic Models ◽  
Region I ◽  
History Of ◽  
State Models ◽  
The Relationship

Two-state models (telegraph-like models) have a successful history of predicting distributions of cellular and nascent mRNA numbers that can well fit experimental data. These models exclude key rate limiting steps, and hence it is unclear why they are able to accurately predict the number distributions. To answer this question, here we compare these models to a novel stochastic mechanistic model of transcription in mammalian cells that presents a unified description of transcriptional factor, polymerase and mature mRNA dynamics. We show that there is a large region of parameter space where the first, second and third moments of the distributions of the waiting times between two consecutively produced transcripts (nascent or mature) of two-state and mechanistic models exactly match. In this region, (i) one can uniquely express the parameters of the two-state models in terms of those of the mechanistic model, (ii) the models are practically indistinguishable by comparison of their transcript numbers distributions, and (iii) they are distinguishable from the shape of their waiting time distributions. Our results clarify the relationship between different gene expression models and identify a means to select between them from experimental data.

scholarly journals Distinguishing between models of mammalian gene expression: telegraph-like models versus mechanistic models

2021 ◽  
Vol 18 (183) ◽  
Author(s):  
Svitlana Braichenko ◽  
James Holehouse ◽  
Ramon Grima
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Mammalian Cells ◽  
Mechanistic Model ◽  
Waiting Times ◽  
Model Parameters ◽  
Mechanistic Models ◽  
History Of ◽  
State Models ◽  
Two State Model

Two-state models (telegraph-like models) have a successful history of predicting distributions of cellular and nascent mRNA numbers that can well fit experimental data. These models exclude key rate limiting steps, and hence it is unclear why they are able to accurately predict the number distributions. To answer this question, here we compare these models to a novel stochastic mechanistic model of transcription in mammalian cells that presents a unified description of transcriptional factor, polymerase and mature mRNA dynamics. We show that there is a large region of parameter space where the first, second and third moments of the distributions of the waiting times between two consecutively produced transcripts (nascent or mature) of two-state and mechanistic models exactly match. In this region: (i) one can uniquely express the two-state model parameters in terms of those of the mechanistic model, (ii) the models are practically indistinguishable by comparison of their transcript numbers distributions, and (iii) they are distinguishable from the shape of their waiting time distributions. Our results clarify the relationship between different gene expression models and identify a means to select between them from experimental data.

scholarly journals Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

2019 ◽  
Author(s):  
Marina Esteban ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Joaquín Dopazo
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Fanconi Anemia ◽  
Rare Diseases ◽  
Predictive Models ◽  
Mechanistic Model ◽  
Genomic Data ◽  
Potential Effect ◽  
Mechanistic Models ◽  
Related Cell

AbstractBackgroundIn spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.ResultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.ConclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

scholarly journals Placental genomic risk scores and early neurodevelopmental outcomes

2021 ◽  
Vol 118 (7) ◽  
pp. e2019789118
Author(s):  
Gianluca Ursini ◽  
Giovanna Punzi ◽  
Benjamin W. Langworthy ◽  
Qiang Chen ◽  
Kai Xia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Developmental Disorders ◽  
Brain Volume ◽  
Risk Scores ◽  
Neurodevelopmental Outcomes ◽  
History Of ◽  
And Function ◽  
Relationship Of ◽  
The Relationship ◽  
Genomic Risk

Tracing the early paths leading to developmental disorders is critical for prevention. In previous work, we detected an interaction between genomic risk scores for schizophrenia (GRSs) and early-life complications (ELCs), so that the liability of the disorder explained by genomic risk was higher in the presence of a history of ELCs, compared with its absence. This interaction was specifically driven by loci harboring genes highly expressed in placentae from normal and complicated pregnancies [G. Ursini et al., Nat. Med. 24, 792–801 (2018)]. Here, we analyze whether fractionated genomic risk scores for schizophrenia and other developmental disorders and traits, based on placental gene-expression loci (PlacGRSs), are linked with early neurodevelopmental outcomes in individuals with a history of ELCs. We found that schizophrenia’s PlacGRSs are negatively associated with neonatal brain volume in singletons and offspring of multiple pregnancies and, in singletons, with cognitive development at 1 y and, less strongly, at 2 y, when cognitive scores become more sensitive to other factors. These negative associations are stronger in males, found only with GRSs fractionated by placental gene expression, and not found in PlacGRSs for other developmental disorders and traits. The relationship of PlacGRSs with brain volume persists as an anlage of placenta biology in adults with schizophrenia, again selectively in males. Higher placental genomic risk for schizophrenia, in the presence of ELCs and particularly in males, alters early brain growth and function, defining a potentially reversible neurodevelopmental path of risk that may be unique to schizophrenia.

scholarly journals Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

2019 ◽  
Author(s):  
Marina Esteban-Medina ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Joaquin Dopazo
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Fanconi Anemia ◽  
Rare Diseases ◽  
Predictive Models ◽  
Mechanistic Model ◽  
Genomic Data ◽  
Potential Effect ◽  
Mechanistic Models ◽  
Related Cell

Abstract Background In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases. Results The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets. Conclusions The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

scholarly journals Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

2019 ◽  
Author(s):  
Marina Esteban ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Joaquin Dopazo
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Fanconi Anemia ◽  
Rare Diseases ◽  
Predictive Models ◽  
Mechanistic Model ◽  
Genomic Data ◽  
Potential Effect ◽  
Mechanistic Models ◽  
Related Cell

Abstract Background: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases. Results: The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets. Conclusions: The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

scholarly journals A mechanistic model of the BLADE platform predicts performance characteristics of 256 different synthetic DNA recombination circuits

2020 ◽  
Author(s):  
Jack E. Bowyer ◽  
Chloe Ding ◽  
Benjamin H. Weinberg ◽  
Wilson W. Wong ◽  
Declan G. Bates
Keyword(s):  
Experimental Data ◽  
Mammalian Cells ◽  
Mechanistic Model ◽  
Dna Recombination ◽  
Boolean Logic ◽  
Circuit Performance ◽  
Synthetic Dna ◽  
Logical Control ◽  
Model Predictions ◽  
Expression In Mammalian Cells

AbstractBoolean logic and arithmetic through DNA excision (BLADE) is a recently developed platform for implementing inducible and logical control over gene expression in mammalian cells, which has the potential to revolutionise cell engineering for therapeutic applications. This 2-input 2-output platform can implement 256 different logical circuits that exploit the specificity and stability of DNA recombination. Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based on Cre- and Flp-mediated DNA excision. After calibrating the model on experimental data from two circuits, we demonstrate close agreement between model outputs and data on the other 111 circuits that have so far been experimentally constructed using the 2-input BLADE platform. Model simulations of the remaining 143 circuits that have yet to be tested experimentally predict excellent performance of the 2-input BLADE platform across the range of possible circuits. Circuits from both the tested and untested subsets that perform less well consist of a disproportionally high number of STOP sequences. Model predictions suggested that circuit performance declines with a decrease in recombinase expression and new experimental data was generated that confirms this relationship.

scholarly journals A mechanistic model of the BLADE platform predicts performance characteristics of 256 different synthetic DNA recombination circuits

2020 ◽  
Vol 16 (12) ◽  
pp. e1007849
Author(s):  
Jack E. Bowyer ◽  
Chloe Ding ◽  
Benjamin H. Weinberg ◽  
Wilson W. Wong ◽  
Declan G. Bates
Keyword(s):  
Experimental Data ◽  
Mammalian Cells ◽  
Mechanistic Model ◽  
Dna Recombination ◽  
Boolean Logic ◽  
Circuit Performance ◽  
Synthetic Dna ◽  
Logical Control ◽  
Model Predictions ◽  
Expression In Mammalian Cells

Boolean logic and arithmetic through DNA excision (BLADE) is a recently developed platform for implementing inducible and logical control over gene expression in mammalian cells, which has the potential to revolutionise cell engineering for therapeutic applications. This 2-input 2-output platform can implement 256 different logical circuits that exploit the specificity and stability of DNA recombination. Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based on Cre- and Flp-mediated DNA excision. After calibrating the model on experimental data from two circuits, we demonstrate close agreement between model outputs and data on the other 111 circuits that have so far been experimentally constructed using the 2-input BLADE platform. Model simulations of the remaining 143 circuits that have yet to be tested experimentally predict excellent performance of the 2-input BLADE platform across the range of possible circuits. Circuits from both the tested and untested subsets that perform less well consist of a disproportionally high number of STOP sequences. Model predictions suggested that circuit performance declines with a decrease in recombinase expression and new experimental data was generated that confirms this relationship.

Does it pay off to link functional gene expression to denitrification rates via modelling?

2020 ◽  
Author(s):  
Anna Störiko ◽  
Holger Pagel ◽  
Olaf Cirpka
Keyword(s):  
Gene Expression ◽  
Mechanistic Model ◽  
Reaction Rates ◽  
Biological Data ◽  
State Variables ◽  
Turnover Rates ◽  
Using Data ◽  
Inhibition Control ◽  
Denitrification Genes ◽  
The Relationship

<p>The abundances of functional genes and transcripts have provided new insights into microbially mediated biogeochemical processes and might improve quantitative predictions of turnover rates.<br>However, the relationship between reaction rates and the gene and transcript abundances may not be a simple correlation.<br>Most mechanistic reaction models cannot predict molecular-biological data, and it is unclear how they can be informed by such data.</p><p>We developed a mechanistic model that considers transcript abundances of denitrification genes, enzyme concentrations, biomass, and solute concentrations as state variables that are interrelated by ordinary differential equations, and thus mechanistically links molecular-biological data to reaction rates.<br>Important features of transcript dynamics can be reproduced with the transcript-based model.</p><p>We calibrated the model using data from a batch experiment with a denitrifying organism at the onset of anoxia.<br>We explored the relationship between transcript abundances and reaction rates by analyzing the model results.<br>The transcript abundances reacted very quickly to substrate concentrations so that we could simplify the model by assuming a quasi steady state of the transcripts.</p><p>We compared our model to a classical Monod-type formulation, which was as good at simulating the concentrations of nitrogen species as the transcript-based model, but it cannot make use of any molecular-biological data.<br>Our results, thus, suggest that enzyme kinetics (substrate limitation, inhibition) control denitrification rates more strongly than the dynamics of gene expression.</p>

The relationship of the scleractinian corals to the rugose corals

Paleobiology ◽  
1980 ◽  
Vol 6 (02) ◽  
pp. 146-160 ◽  
Author(s):  
William A. Oliver
Keyword(s):  
Critical Points ◽  
Scleractinian Corals ◽  
Convincing Evidence ◽  
Early Triassic ◽  
Rugose Corals ◽  
History Of ◽  
The Subject ◽  
Relationship Of ◽  
The Relationship ◽  
Biologic Factors

The Mesozoic-Cenozoic coral Order Scleractinia has been suggested to have originated or evolved (1) by direct descent from the Paleozoic Order Rugosa or (2) by the development of a skeleton in members of one of the anemone groups that probably have existed throughout Phanerozoic time. In spite of much work on the subject, advocates of the direct descent hypothesis have failed to find convincing evidence of this relationship. Critical points are:(1) Rugosan septal insertion is serial; Scleractinian insertion is cyclic; no intermediate stages have been demonstrated. Apparent intermediates are Scleractinia having bilateral cyclic insertion or teratological Rugosa.(2) There is convincing evidence that the skeletons of many Rugosa were calcitic and none are known to be or to have been aragonitic. In contrast, the skeletons of all living Scleractinia are aragonitic and there is evidence that fossil Scleractinia were aragonitic also. The mineralogic difference is almost certainly due to intrinsic biologic factors.(3) No early Triassic corals of either group are known. This fact is not compelling (by itself) but is important in connection with points 1 and 2, because, given direct descent, both changes took place during this only stage in the history of the two groups in which there are no known corals.

Investigating the Relationship Between Self-Esteem and Stigma Among Young Adults With History of Suicide Attempts

Crisis ◽  
2016 ◽  
Vol 37 (4) ◽  
pp. 265-270 ◽  
Author(s):  
Meshan Lehmann ◽  
Matthew R. Hilimire ◽  
Lawrence H. Yang ◽  
Bruce G. Link ◽  
Jordan E. DeVylder
Keyword(s):  
Young Adults ◽  
Suicide Attempts ◽  
Self Esteem ◽  
High Risk Population ◽  
High Functioning ◽  
People With Mental Illness ◽  
History Of ◽  
The Individual ◽  
The Impact ◽  
The Relationship

Abstract. Background: Self-esteem is a major contributor to risk for repeated suicide attempts. Prior research has shown that awareness of stigma is associated with reduced self-esteem among people with mental illness. No prior studies have examined the association between self-esteem and stereotype awareness among individuals with past suicide attempts. Aims: To understand the relationship between stereotype awareness and self-esteem among young adults who have and have not attempted suicide. Method: Computerized surveys were administered to college students (N = 637). Linear regression analyses were used to test associations between self-esteem and stereotype awareness, attempt history, and their interaction. Results: There was a significant stereotype awareness by attempt interaction (β = –.74, p = .006) in the regression analysis. The interaction was explained by a stronger negative association between stereotype awareness and self-esteem among individuals with past suicide attempts (β = –.50, p = .013) compared with those without attempts (β = –.09, p = .037). Conclusion: Stigma is associated with lower self-esteem within this high-functioning sample of young adults with histories of suicide attempts. Alleviating the impact of stigma at the individual (clinical) or community (public health) levels may improve self-esteem among this high-risk population, which could potentially influence subsequent suicide risk.

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