70,671 research outputs found
Cis-regulatory Analysis Of The Pigment Cell Differentiation Gene Polyketide Synthase
The analysis of Gene Regulatory Networks (GRNs) is essential to understanding the complete process of embryo development. Elucidating every gene regulatory circuit from maternal regulatory inputs all the way to the activation of differentiation gene batteries is an important step in increasing our understanding of developmental biology. In this work I study the cis-regulatory architecture of a pigment cell differentiation gene, polyketide synthase (SpPks) in the sea urchin Strongylocentrotus purpuratus. SpPks encodes an enzyme that is responsible for the biosynthesis of the sea urchin pigment echinochrome in larval pigment cells. The analysis of the promoter of a differentiation gene will lead to identifying the direct upstream regulators and ultimately to elucidating the structure of the upstream gene regulatory network, which is mostly uncharacterized. From previous studies the transcription factors SpGcm and SpGatae are predicted to be positive regulators of SpPks. Here, I identify a minimal 1kb promoter region containing putative DNA-binding sites for both GCM and GATAE that is able to recapitulate the expression of SpPks. I further show by mutagenesis that a putative DNA-binding site for GCM located 1,179 base pairs upstream of the start of transcription is a direct target for the positive cis-regulation of SpPks. Quantitative analysis of the transcriptional regulatory function of the GCM-mutagenized construct suggests that GCM is not necessary for the start of SpPks transcription but is required for its maintenance. Several GATA E binding sites have been identified within the minimal promoter for SpPks by means of consensus sequence. My analysis suggests that GATA E may be a direct positive regulator and could potentially be required for the onset of transcription of SpPks, though further experimentation will be necessary to characterize the exact regulatory function of GATA E
Emergence of switch-like behavior in a large family of simple biochemical networks
Bistability plays a central role in the gene regulatory networks (GRNs)
controlling many essential biological functions, including cellular
differentiation and cell cycle control. However, establishing the network
topologies that can exhibit bistability remains a challenge, in part due to the
exceedingly large variety of GRNs that exist for even a small number of
components. We begin to address this problem by employing chemical reaction
network theory in a comprehensive in silico survey to determine the capacity
for bistability of more than 40,000 simple networks that can be formed by two
transcription factor-coding genes and their associated proteins (assuming only
the most elementary biochemical processes). We find that there exist reaction
rate constants leading to bistability in ~90% of these GRN models, including
several circuits that do not contain any of the TF cooperativity commonly
associated with bistable systems, and the majority of which could only be
identified as bistable through an original subnetwork-based analysis. A
topological sorting of the two-gene family of networks based on the presence or
absence of biochemical reactions reveals eleven minimal bistable networks
(i.e., bistable networks that do not contain within them a smaller bistable
subnetwork). The large number of previously unknown bistable network topologies
suggests that the capacity for switch-like behavior in GRNs arises with
relative ease and is not easily lost through network evolution. To highlight
the relevance of the systematic application of CRNT to bistable network
identification in real biological systems, we integrated publicly available
protein-protein interaction, protein-DNA interaction, and gene expression data
from Saccharomyces cerevisiae, and identified several GRNs predicted to behave
in a bistable fashion.Comment: accepted to PLoS Computational Biolog
Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development
Progress in cell type reprogramming has revived the interest in Waddington's
concept of the epigenetic landscape. Recently researchers developed the
quasi-potential theory to represent the Waddington's landscape. The
Quasi-potential U(x), derived from interactions in the gene regulatory network
(GRN) of a cell, quantifies the relative stability of network states, which
determine the effort required for state transitions in a multi-stable dynamical
system. However, quasi-potential landscapes, originally developed for
continuous systems, are not suitable for discrete-valued networks which are
important tools to study complex systems. In this paper, we provide a framework
to quantify the landscape for discrete Boolean networks (BNs). We apply our
framework to study pancreas cell differentiation where an ensemble of BN models
is considered based on the structure of a minimal GRN for pancreas development.
We impose biologically motivated structural constraints (corresponding to
specific type of Boolean functions) and dynamical constraints (corresponding to
stable attractor states) to limit the space of BN models for pancreas
development. In addition, we enforce a novel functional constraint
corresponding to the relative ordering of attractor states in BN models to
restrict the space of BN models to the biological relevant class. We find that
BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics
of pancreas cell differentiation. This framework can also determine the genes'
influence on cell state transitions, and thus can facilitate the rational
design of cell reprogramming protocols.Comment: 24 pages, 6 figures, 1 tabl
A Method to Identify and Analyze Biological Programs through Automated Reasoning.
Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function
The tailless Ortholog nhr-67 Regulates Patterning of Gene Expression and Morphogenesis in the C. elegans Vulva
Regulation of spatio-temporal gene expression in diverse cell and tissue types is a critical aspect of development. Progression through Caenorhabditis elegans vulval development leads to the generation of seven distinct vulval cell types (vulA, vulB1, vulB2, vulC, vulD, vulE, and vulF), each with its own unique gene expression profile. The mechanisms that establish the precise spatial patterning of these mature cell types are largely unknown. Dissection of the gene regulatory networks involved in vulval patterning and differentiation would help us understand how cells generate a spatially defined pattern of cell fates during organogenesis. We disrupted the activity of 508 transcription factors via RNAi and assayed the expression of ceh-2, a marker for vulB fate during the L4 stage. From this screen, we identified the tailless ortholog nhr-67 as a novel regulator of gene expression in multiple vulval cell types. We find that one way in which nhr-67 maintains cell identity is by restricting inappropriate cell fusion events in specific vulval cells, namely vulE and vulF. nhr-67 exhibits a dynamic expression pattern in the vulval cells and interacts with three other transcriptional regulators cog-1 (Nkx6.1/6.2), lin-11 (LIM), and egl-38 (Pax2/5/8) to generate the composite expression patterns of their downstream targets. We provide evidence that egl-38 regulates gene expression in vulB1, vulC, vulD, vulE, as well as vulF cells. We demonstrate that the pairwise interactions between these regulatory genes are complex and vary among the seven cell types. We also discovered a striking regulatory circuit that affects a subset of the vulval lineages: cog-1 and nhr-67 inhibit both one another and themselves. We postulate that the differential levels and combinatorial patterns of lin-11, cog-1, and nhr-67 expression are a part of a regulatory code for the mature vulval cell types
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
- …