48,663 research outputs found
Phenotypic Variation and Bistable Switching in Bacteria
Microbial research generally focuses on clonal populations. However, bacterial cells with identical genotypes frequently display different phenotypes under identical conditions. This microbial cell individuality is receiving increasing attention in the literature because of its impact on cellular differentiation, survival under selective conditions, and the interaction of pathogens with their hosts. It is becoming clear that stochasticity in gene expression in conjunction with the architecture of the gene network that underlies the cellular processes can generate phenotypic variation. An important regulatory mechanism is the so-called positive feedback, in which a system reinforces its own response, for instance by stimulating the production of an activator. Bistability is an interesting and relevant phenomenon, in which two distinct subpopulations of cells showing discrete levels of gene expression coexist in a single culture. In this chapter, we address techniques and approaches used to establish phenotypic variation, and relate three well-characterized examples of bistability to the molecular mechanisms that govern these processes, with a focus on positive feedback.
Synthetic biology: advancing biological frontiers by building synthetic systems
Advances in synthetic biology are contributing
to diverse research areas, from basic biology to
biomanufacturing and disease therapy. We discuss the
theoretical foundation, applications, and potential of
this emerging field
Critical Transitions In a Model of a Genetic Regulatory System
We consider a model for substrate-depletion oscillations in genetic systems,
based on a stochastic differential equation with a slowly evolving external
signal. We show the existence of critical transitions in the system. We apply
two methods to numerically test the synthetic time series generated by the
system for early indicators of critical transitions: a detrended fluctuation
analysis method, and a novel method based on topological data analysis
(persistence diagrams).Comment: 19 pages, 8 figure
Network-based approaches to explore complex biological systems towards network medicine
Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes
Gene regulatory networks: a coarse-grained, equation-free approach to multiscale computation
We present computer-assisted methods for analyzing stochastic models of gene
regulatory networks. The main idea that underlies this equation-free analysis
is the design and execution of appropriately-initialized short bursts of
stochastic simulations; the results of these are processed to estimate
coarse-grained quantities of interest, such as mesoscopic transport
coefficients. In particular, using a simple model of a genetic toggle switch,
we illustrate the computation of an effective free energy and of a
state-dependent effective diffusion coefficient that characterize an
unavailable effective Fokker-Planck equation. Additionally we illustrate the
linking of equation-free techniques with continuation methods for performing a
form of stochastic "bifurcation analysis"; estimation of mean switching times
in the case of a bistable switch is also implemented in this equation-free
context. The accuracy of our methods is tested by direct comparison with
long-time stochastic simulations. This type of equation-free analysis appears
to be a promising approach to computing features of the long-time,
coarse-grained behavior of certain classes of complex stochastic models of gene
regulatory networks, circumventing the need for long Monte Carlo simulations.Comment: 33 pages, submitted to The Journal of Chemical Physic
Synthetic biology—putting engineering into biology
Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis—synthetic biology’s system fabrication process—supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.
Designer Gene Networks: Towards Fundamental Cellular Control
The engineered control of cellular function through the design of synthetic
genetic networks is becoming plausible. Here we show how a naturally occurring
network can be used as a parts list for artificial network design, and how
model formulation leads to computational and analytical approaches relevant to
nonlinear dynamics and statistical physics.Comment: 35 pages, 8 figure
Engineering ligand-responsive RNA controllers in yeast through the assembly of RNase III tuning modules
The programming of cellular networks to achieve new biological functions depends on the development of genetic tools that link the presence of a molecular signal to gene-regulatory activity. Recently, a set of engineered RNA controllers was described that enabled predictable tuning of gene expression in the yeast Saccharomyces cerevisiae through directed cleavage of transcripts by an RNase III enzyme, Rnt1p. Here, we describe a strategy for building a new class of RNA sensing-actuation devices based on direct integration of RNA aptamers into a region of the Rnt1p hairpin that modulates Rnt1p cleavage rates. We demonstrate that ligand binding to the integrated aptamer domain is associated with a structural change sufficient to inhibit Rnt1p processing. Three tuning strategies based on the incorporation of different functional modules into the Rnt1p switch platform were demonstrated to optimize switch dynamics and ligand responsiveness. We further demonstrated that these tuning modules can be implemented combinatorially in a predictable manner to further improve the regulatory response properties of the switch. The modularity and tunability of the Rnt1p switch platform will allow for rapid optimization and tailoring of this gene control device, thus providing a useful tool for the design of complex genetic networks in yeast
Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data
Determining the functional structure of biological networks is a central goal
of systems biology. One approach is to analyze gene expression data to infer a
network of gene interactions on the basis of their correlated responses to
environmental and genetic perturbations. The inferred network can then be
analyzed to identify functional communities. However, commonly used algorithms
can yield unreliable results due to experimental noise, algorithmic
stochasticity, and the influence of arbitrarily chosen parameter values.
Furthermore, the results obtained typically provide only a simplistic view of
the network partitioned into disjoint communities and provide no information of
the relationship between communities. Here, we present methods to robustly
detect coregulated and functionally enriched gene communities and demonstrate
their application and validity for Escherichia coli gene expression data.
Applying a recently developed community detection algorithm to the network of
interactions identified with the context likelihood of relatedness (CLR)
method, we show that a hierarchy of network communities can be identified.
These communities significantly enrich for gene ontology (GO) terms, consistent
with them representing biologically meaningful groups. Further, analysis of the
most significantly enriched communities identified several candidate new
regulatory interactions. The robustness of our methods is demonstrated by
showing that a core set of functional communities is reliably found when
artificial noise, modeling experimental noise, is added to the data. We find
that noise mainly acts conservatively, increasing the relatedness required for
a network link to be reliably assigned and decreasing the size of the core
communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1
was not uploaded but is available by contacting the author. 27 pages, 5
figures, 15 supplementary file
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