Interagency Modeling and Analysis Group

scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

What is being modeled?

Cellular trajectories, transition probabilities, energy landscape.

Description & purpose of resource

scEpath is an approach that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories.

Spatial scales

molecular

cellular

Temporal scales

1 - 103 s

hours

days

weeks to months

This resource is currently

mature and useful in ongoing research

Has this resource been validated?

Yes

Link to Resource Credibility Assessment

scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)

Yes

Key publications (e.g. describing or using resource)

Suoqin Jin, Adam L. MacLean, Tao Peng, Qing Nie. scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data. Bioinformatics. 2018 Jun 15;34(12):2077-2086. doi: 10.1093/bioinformatics/bty058.

DOI link to publication describing this resource

10.1093/bioinformatics/bty058

Link to resource

scEpath package

Collaborators

Qing Nie

PI contact information

qnie@uci.edu

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Prediction of metabolite concentrations, rate constants and post-translational regulation

What is being modeled?

Central Metabolism

Description & purpose of resource

This tool consists of Jupyter notebooks, contained in the Supplementary Information to the linked article, for a new approach to modeling the mass action kinetics of metabolism. The central metabolism of Neurospora crassa, a filamentous fungi, is used for demonstration purposes.

Spatial scales

molecular

cellular

Temporal scales

<10-6 s (chemical reactions)

10-6 - 10-3 s

This resource is currently

a demonstration or a framework to be built upon (perhaps with a sample implementation)

Has this resource been validated?

N/A

Can this resource be associated with other resources? (e.g.: modular models, linked tools and platforms)

Yes

Which resources?

The eQuilibrator tool for estimating standard free energies of metabolic reactions: http://equilibrator.weizmann.ac.il/.

Key publications (e.g. describing or using resource)

Cannon, W.R., et al., Prediction of metabolite concentrations, rate constants and post-translational regulation using maximum entropy-based simulations with application to central metabolism of Neurospora crassa. Processes, 2018. 6(6).

DOI link to publication describing this resource

https://doi.org/10.3390/pr6060063

Link to resource

Supplementary Material

Collaborators

Bill Cannon

PI contact information

william.cannon@pnnl.gov

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Circadian Proteomic Analysis Uncovers Mechanisms of Post-Transcriptional Regulation in Metabolic Pathways

Read more about Circadian Proteomic Analysis Uncovers Mechanisms of Post-Transcriptional Regulation in Metabolic Pathways

Transcriptional and translational feedback loops in fungi and animals drive circadian rhythms in transcript levels that provide output from the clock, but post-transcriptional mechanisms also contribute. To determine the extent and underlying source of this regulation, we applied newly developed analytical tools to a long-duration, deeply sampled, circadian proteomics time course comprising half of the proteome.

A Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

Read more about A Prediction of Metabolite Concentrations, Rate Constants and Post-Translational Regulation Using Maximum Entropy-Based Simulations with Application to Central Metabolism of Neurospora crassa

We report the application of a recently proposed approach for modeling biological systems using a maximum entropy production rate principle in lieu of having in vivo rate constants.

Non-steady state mass action dynamics without rate constants: dynamics of coupled reactions using chemical potentials

Read more about Non-steady state mass action dynamics without rate constants: dynamics of coupled reactions using chemical potentials

Comprehensive and predictive simulation of coupled reaction networks has long been a goal of biology and other fields. Currently, metabolic network models that utilize enzyme mass action kinetics have predictive power but are limited in scope and application by the fact that the determination of enzyme rate constants is laborious and low throughput. We present a statistical thermodynamic formulation of the law of mass action for coupled reactions at both steady states and non-stationary states. The formulation uses chemical potentials instead of rate constants.