6,108 research outputs found
A stochastic and dynamical view of pluripotency in mouse embryonic stem cells
Pluripotent embryonic stem cells are of paramount importance for biomedical
research thanks to their innate ability for self-renewal and differentiation
into all major cell lines. The fateful decision to exit or remain in the
pluripotent state is regulated by complex genetic regulatory network. Latest
advances in transcriptomics have made it possible to infer basic topologies of
pluripotency governing networks. The inferred network topologies, however, only
encode boolean information while remaining silent about the roles of dynamics
and molecular noise in gene expression. These features are widely considered
essential for functional decision making. Herein we developed a framework for
extending the boolean level networks into models accounting for individual
genetic switches and promoter architecture which allows mechanistic
interrogation of the roles of molecular noise, external signaling, and network
topology. We demonstrate the pluripotent state of the network to be a broad
attractor which is robust to variations of gene expression. Dynamics of exiting
the pluripotent state, on the other hand, is significantly influenced by the
molecular noise originating from genetic switching events which makes cells
more responsive to extracellular signals. Lastly we show that steady state
probability landscape can be significantly remodeled by global gene switching
rates alone which can be taken as a proxy for how global epigenetic
modifications exert control over stability of pluripotent states.Comment: 11 pages, 7 figure
Genome-wide discovery of modulators of transcriptional interactions in human B lymphocytes
Transcriptional interactions in a cell are modulated by a variety of
mechanisms that prevent their representation as pure pairwise interactions
between a transcription factor and its target(s). These include, among others,
transcription factor activation by phosphorylation and acetylation, formation
of active complexes with one or more co-factors, and mRNA/protein degradation
and stabilization processes.
This paper presents a first step towards the systematic, genome-wide
computational inference of genes that modulate the interactions of specific
transcription factors at the post-transcriptional level. The method uses a
statistical test based on changes in the mutual information between a
transcription factor and each of its candidate targets, conditional on the
expression of a third gene. The approach was first validated on a synthetic
network model, and then tested in the context of a mammalian cellular system.
By analyzing 254 microarray expression profiles of normal and tumor related
human B lymphocytes, we investigated the post transcriptional modulators of the
MYC proto-oncogene, an important transcription factor involved in
tumorigenesis. Our method discovered a set of 100 putative modulator genes,
responsible for modulating 205 regulatory relationships between MYC and its
targets. The set is significantly enriched in molecules with function
consistent with their activities as modulators of cellular interactions,
recapitulates established MYC regulation pathways, and provides a notable
repertoire of novel regulators of MYC function. The approach has broad
applicability and can be used to discover modulators of any other transcription
factor, provided that adequate expression profile data are available.Comment: 15 pages, 3 figures, 2 tables; minor changes following referees'
comments; accepted to RECOMB0
Inferring the function of genes from synthetic lethal mutations
Techniques for detecting synthetic lethal mutations in double gene deletion experiments are emerging as powerful tool for analysing genes in parallel or overlapping pathways with a shared function. This paper introduces a logic-based approach that uses synthetic lethal mutations for mapping genes of unknown function to enzymes in a known metabolic network. We show how such mappings can be automatically computed by a logical learning system called eXtended Hybrid Abductive Inductive Learning (XHAIL)
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