Matrix formalism to describe functional states of transcriptional regulatory systems.
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ABSTRACT:
SUBMITTER: Gianchandani EP
PROVIDER: S-EPMC1534074 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A transcriptional regulatory network (TRN) constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS]) and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this "pseudo-stoichiometric" matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R) to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1) a gene expression correlation matrix delineating functional motifs; (2) sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3) the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks.
| S-EPMC2685017 | biostudies-literature
Project description:A principal aim of systems biology is to develop in silico models of whole cells or cellular processes that explain and predict observable cellular phenotypes. Here, we use a model of a genome-scale reconstruction of the integrated metabolic and transcriptional regulatory networks for Escherichia coli, composed of 1,010 gene products, to assess the properties of all functional states computed in 15,580 different growth environments. The set of all functional states of the integrated network exhibits a discernable structure that can be visualized in 3-dimensional space, showing that the transcriptional regulatory network governing metabolism in E. coli responds primarily to the available electron acceptor and the presence of glucose as the carbon source. This result is consistent with recently published experimental data. The observation that a complex network composed of 1,010 genes is organized to achieve few dominant modes demonstrates the utility of the systems approach for consolidating large amounts of genome-scale molecular information about a genome and its regulation to elucidate an organism's preferred environments and functional capabilities.
| S-EPMC1323155 | biostudies-literature
Project description:Gene expression is controlled by interactions between trans-regulatory factors and cis-regulatory DNA sequences, and these interactions constitute the essential functional linkages of gene regulatory networks (GRNs). Validation of GRN models requires experimental cis-regulatory tests of predicted linkages to authenticate their identities and proposed functions. However, cis-regulatory analysis is, at present, at a severe bottleneck in genomic system biology because of the demanding experimental methodologies currently in use for discovering cis-regulatory modules (CRMs), in the genome, and for measuring their activities. Here we demonstrate a high-throughput approach to both discovery and quantitative characterization of CRMs. The unique aspect is use of DNA sequence tags to "barcode" CRM expression constructs, which can then be mixed, injected together into sea urchin eggs, and subsequently deconvolved. This method has increased the rate of cis-regulatory analysis by >100-fold compared with conventional one-by-one reporter assays. The utility of the DNA-tag reporters was demonstrated by the rapid discovery of 81 active CRMs from 37 previously unexplored sea urchin genes. We then obtained simultaneous high-resolution temporal characterization of the regulatory activities of more than 80 CRMs. On average 2-3 CRMs were discovered per gene. Comparison of endogenous gene expression profiles with those of the CRMs recovered from each gene showed that, for most cases, at least one CRM is active in each phase of endogenous expression, suggesting that CRM recovery was comprehensive. This approach will qualitatively alter the practice of GRN construction as well as validation, and will impact many additional areas of regulatory system biology.
| S-EPMC2840491 | biostudies-literature
Project description:PURPOSE:An extended phase graph framework (EPG-X) for modeling systems with exchange or magnetization transfer (MT) is proposed. THEORY:EPG-X models coupled two-compartment systems by describing each compartment with separate phase graphs that exchange during evolution periods. There are two variants: EPG-X(BM) for systems governed by the Bloch-McConnell equations, and EPG-X(MT) for the pulsed MT formalism. For the MT case, the "bound" protons have no transverse components, so their phase graph consists of only longitudinal states. METHODS:The EPG-X model was validated against steady-state solutions and isochromat-based simulation of gradient-echo sequences. Three additional test cases were investigated: (i) MT effects in multislice turbo spin-echo; (ii) variable flip angle gradient-echo imaging of the type used for MR fingerprinting; and (iii) water exchange in multi-echo spin-echo T2 relaxometry. RESULTS:EPG-X was validated successfully against isochromat based transient simulations and known steady-state solutions. EPG-X(MT) simulations matched in-vivo measurements of signal attenuation in white matter in multislice turbo spin-echo images. Magnetic resonance fingerprinting-style experiments with a bovine serum albumin (MT) phantom showed that the data were not consistent with a single-pool model, but EPG-X(MT) could be used to fit the data well. The EPG-X(BM) simulations of multi-echo spin-echo T2 relaxometry suggest that exchange could lead to an underestimation of the myelin-water fraction. CONCLUSIONS:The EPG-X framework can be used for modeling both steady-state and transient signal response of systems exhibiting exchange or MT. This may be particularly beneficial for relaxometry approaches that rely on characterizing transient rather than steady-state sequences. Magn Reson Med 80:767-779, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
| S-EPMC5947218 | biostudies-literature
Project description:Modeling optical tweezers in the T-matrix formalism has been of key importance for accurate and efficient calculations of optical forces and their comparison with experiments. Here we extend this formalism to the modeling of chiral optomechanics and optical tweezers where chiral light is used for optical manipulation and trapping of optically active particles. We first use the Bohren decomposition to deal with the light scattering of chiral light on optically active particles. Thus, we show analytically that all the observables (cross sections, asymmetry parameters) are split into a helicity dependent and independent part and study a practical example of a complex resin particle with inner copper-coated stainless steel helices. Then, we apply this chiral T-matrix framework to optical tweezers where a tightly focused chiral field is used to trap an optically active spherical particle, calculate the chiral behaviour of optical trapping stiffnesses and their size scaling, and extend calculations to chiral nanowires and clusters of astrophysical interest. Such general light scattering framework opens perspectives for modeling optical forces on biological materials where optically active amino acids and carbohydrates are present.
| S-EPMC6328542 | biostudies-literature
Project description:BackgroundHigh throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome.ResultsWe constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity.ConclusionTranscription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.
| S-EPMC1482723 | biostudies-literature
Project description:The rapid progress in quantum-optical experiments, especially in the field of cavity quantum electrodynamics and nanoplasmonics, allows one to substantially modify and control chemical and physical properties of atoms, molecules, and solids by strongly coupling to the quantized field. Alongside such experimental advances has been the recent development of ab initio approaches such as quantum electrodynamical density-functional theory (QEDFT), which is capable of describing these strongly coupled systems from first principles. To investigate response properties of relatively large systems coupled to a wide range of photon modes, ab initio methods that scale well with system size become relevant. In light of this, we extend the linear-response Sternheimer approach within the framework of QEDFT to efficiently compute excited-state properties of strongly coupled light-matter systems. Using this method, we capture features of strong light-matter coupling both in the dispersion and absorption properties of a molecular system strongly coupled to the modes of a cavity. We exemplify the efficiency of the Sternheimer approach by coupling the matter system to the continuum of an electromagnetic field. We observe changes in the spectral features of the coupled system as Lorentzian line shapes turn into Fano resonances when the molecule interacts strongly with the continuum of modes. This work provides an alternative approach for computing efficiently excited-state properties of large molecular systems interacting with the quantized electromagnetic field.
| S-EPMC9281401 | biostudies-literature
Project description:Taking advantage of available functional data associated with 115 transcription and 7 sigma factors, we have performed a structural analysis of the regulatory network of Escherichia coli. While the mode of regulatory interaction between transcription factors (TFs) is predominantly positive, TFs are frequently negatively autoregulated. Furthermore, feedback loops, regulatory motifs and regulatory pathways are unevenly distributed in this network. Short pathways, multiple feed-forward loops and negative autoregulatory interactions are particularly predominant in the subnetwork controlling metabolic functions such as the use of alternative carbon sources. In contrast, long hierarchical cascades and positive autoregulatory loops are overrepresented in the subnetworks controlling developmental processes for biofilm and chemotaxis. We propose that these long transcriptional cascades coupled with regulatory switches (positive loops) for external sensing enable the coexistence of multiple bacterial phenotypes. In contrast, short regulatory pathways and negative autoregulatory loops enable an efficient homeostatic control of crucial metabolites despite external variations. TFs at the core of the network coordinate the most basic endogenous processes by passing information onto multi-element circuits. Transcriptional expression data support broader and higher transcription of global TFs compared to specific ones. Global regulators are also more broadly conserved than specific regulators in bacteria, pointing to varying functional constraints.
| S-EPMC2726282 | biostudies-literature
Project description:Transcriptional riboswitches modulate downstream gene expression by a tight coupling of ligand-dependent RNA folding kinetics with the rate of transcription. RNA folding pathways leading to functional ON and OFF regulation involve the formation of metastable states within well-defined sequence intervals during transcription. The kinetic requirements for the formation and preservation of these metastable states in the context of transcription remain unresolved. Here, we reversibly trap the previously defined regulatory relevant metastable intermediate of the Mesoplasma florum 2'-deoxyguanosine (2'dG)-sensing riboswitch using a photocaging-ligation approach, and monitor folding to its native state by real-time NMR in both presence and absence of ligand. We further determine transcription rates for two different bacterial RNA polymerases. Our results reveal that the riboswitch functions only at transcription rates typical for bacterial polymerases (10-50?nt?s-1) and that gene expression is modulated by 40-50% only, while subtle differences in folding rates guide population ratios within the structural ensemble to a specific regulatory outcome.
| S-EPMC5838219 | biostudies-literature
Project description:We examine how different transcriptional network structures can evolve from an ancestral network. By characterizing how the ancestral mode of gene regulation for genes specific to a-type cells in yeast species evolved from an activating paradigm to a repressing one, we show that regulatory protein modularity, conversion of one cis-regulatory sequence to another, distribution of binding energy among protein-protein and protein-DNA interactions, and exploitation of ancestral network features all contribute to the evolution of a novel regulatory mode. The formation of this derived mode of regulation did not disrupt the ancestral mode and thereby created a hybrid regulatory state where both means of transcription regulation (ancestral and derived) contribute to the conserved expression pattern of the network. Finally, we show how this hybrid regulatory state has resolved in different ways in different lineages to generate the diversity of regulatory network structures observed in modern species.
| S-EPMC3519278 | biostudies-literature