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Cell Specific Gene Expression

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Updated: Mar 15, 2026

Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

Published on: December 28, 2011

BTR: training asynchronous Boolean models using single-cell expression data.

Chee Yee Lim¹, Huange Wang¹, Steven Woodhouse¹

¹Department of Haematology, Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge, CB2 0XY, UK.

BMC Bioinformatics

|September 8, 2016

Summary

This summary is machine-generated.

We developed BTR, a new algorithm for training Boolean models with single-cell genomics data. BTR effectively refines and reconstructs gene regulatory networks, outperforming existing methods and handling noisy data robustly.

Keywords:

Asynchronous Boolean model BOOLEAN scoring function Executable model Model learning Network reconstruction Single-cell gene expression

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Single-cell genomics generates noisy expression data with technical artifacts like drop-outs.
Existing gene regulatory network inference algorithms struggle with this noise.
New bioinformatics approaches are needed to effectively analyze single-cell expression data.

Purpose of the Study:

Introduce BTR, an algorithm for training asynchronous Boolean models using single-cell expression data.
Refine existing Boolean models and reconstruct new ones by improving model-data fit.
Address the challenges of noise and drop-outs in single-cell genomics for network inference.

Main Methods:

Developed BTR, an algorithm utilizing a novel Boolean state space scoring function.
Trained asynchronous Boolean models with single-cell expression data.
Compared BTR's Boolean scoring function against the BIC scoring function for Bayesian networks.

Main Results:

BTR demonstrated superior performance in refining and reconstructing Boolean models.
The Boolean scoring function outperformed the BIC scoring function.
BTR outperformed other network inference algorithms on synthetic bulk and single-cell expression data.
Case studies showed BTR's ability to generate new biological insights by improving published models.

Conclusions:

BTR offers a novel method for Boolean model refinement and reconstruction with single-cell data.
Boolean models are robust to drop-outs, making them suitable for noisy single-cell data.
BTR's minimal assumptions facilitate gene regulatory network reconstruction.
BTR has broad potential impact across biomedical research due to its simplicity and the rise of single-cell genomics.