Functional States of the Genome-Scale Escherichia Coli Transcriptional Regulatory System

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

Matrix Formalism to Describe Functional States of Transcriptional Regulatory Systems

Complex regulatory networks control the transcription state of a genome. These transcriptional regulatory networks (TRNs) have been mathematically described using a Boolean formalism, in which the state of a gene is represented as either transcribed or not transcribed in response to regulatory signals. The Boolean formalism results in a series of regulatory rules for the individual genes of a TRN that in turn can be used to link environmental cues to the transcription state of a genome, thereby forming a complete transcriptional regulatory system (TRS). Herein, we develop a formalism that represents such a set of regulatory rules in a matrix form. Matrix formalism allows for the systemic characterization of the properties of a TRS and facilitates the computation of the transcriptional state of the genome under any given set of environmental conditions. Additionally, it provides a means to incorporate mechanistic detail of a TRS as it becomes available. In this study, the regulatory network matrix, R, for a prototypic TRS is characterized and the fundamental subspaces of this matrix are described. We illustrate how the matrix representation of a TRS coupled with its environment (R*) allows for a sampling of all possible expression states of a given network, and furthermore, how the fundamental subspaces of the matrix provide a way to study key TRS features and may assist in experimental design

Dynamic analysis of integrated signaling, metabolic, and regulatory networks.

Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)-based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for "fast" reactions and incorporates "slow" reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporated kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and regulatory processes at the genome scale, such as the S. cerevisiae system presented here

Predicting biological system objectives de novo from internal state measurements

<p>Abstract</p> <p>Background</p> <p>Optimization theory has been applied to complex biological systems to interrogate network properties and develop and refine metabolic engineering strategies. For example, methods are emerging to engineer cells to optimally produce byproducts of commercial value, such as bioethanol, as well as molecular compounds for disease therapy. Flux balance analysis (FBA) is an optimization framework that aids in this interrogation by generating predictions of optimal flux distributions in cellular networks. Critical features of FBA are the definition of a biologically relevant objective function (e.g., maximizing the rate of synthesis of biomass, a unit of measurement of cellular growth) and the subsequent application of linear programming (LP) to identify fluxes through a reaction network. Despite the success of FBA, a central remaining challenge is the definition of a network objective with biological meaning.</p> <p>Results</p> <p>We present a novel method called <b>Biological Objective Solution Search (BOSS) </b>for the inference of an objective function of a biological system from its underlying network stoichiometry as well as experimentally-measured state variables. Specifically, <b>BOSS </b>identifies a system objective by defining a putative stoichiometric "objective reaction," adding this reaction to the existing set of stoichiometric constraints arising from known interactions within a network, and maximizing the putative objective reaction via LP, all the while minimizing the difference between the resultant <it>in silico </it>flux distribution and available experimental (e.g., isotopomer) flux data. This new approach allows for discovery of objectives with previously unknown stoichiometry, thus extending the biological relevance from earlier methods. We verify our approach on the well-characterized central metabolic network of <it>Saccharomyces cerevisiae</it>.</p> <p>Conclusion</p> <p>We illustrate how <b>BOSS </b>offers insight into the functional organization of biochemical networks, facilitating the interrogation of cellular design principles and development of cellular engineering applications. Furthermore, we describe how growth is the best-fit objective function for the yeast metabolic network given experimentally-measured fluxes.</p

Predicting biological system objectives de novo from internal state measurements-2

<p><b>Copyright information:</b></p><p>Taken from "Predicting biological system objectives de novo from internal state measurements"</p><p>http://www.biomedcentral.com/1471-2105/9/43</p><p>BMC Bioinformatics 2008;9():43-43.</p><p>Published online 24 Jan 2008</p><p>PMCID:PMC2258290.</p><p></p> are illustrated and contrasted by the hypothesized objective reaction (i.e., precursor biomass synthesis) (open black circles). Panel (a) illustrates the results when the hypothesized objective reaction of precursor biomass synthesis is excluded from the network inputted into . Panel (b) depicts the results when the network is inputted into with the hypothesized objective reaction. Note that the entire experimental flux distribution was provided as input to in both cases. When the precursor biomass synthesis objective reaction was excluded from the network inputted into , the sum-squared error between the -derived objective reaction and the expected precursor biomass synthesis objective reaction, normalized to the magnitude of the precursor biomass synthesis objective reaction (SSE), was 8.242 × 10(panel (a)). By contrast, when the precursor biomass synthesis objective reaction was included in the set of stoichiometric reactions inputted into , the SSEfor the solution was approximately 655.0 (panel (b)). Panel (c) depicts a plot of the sum-squared error between the -derived and the corresponding reaction when the flux for each of the 62 reactions in the system was excluded from the pool of experimental fluxes one by one. The smallest SSEvalues were 42.20 and 4.210 × 10and corresponded to the ATP maintenance and biomass production reactions, respectively. Consequently, as shown in panel (d), was able to recapitulate the hypothesized objective reaction of precursor biomass synthesis with a SSE= 4.210 × 10when the reaction was included in the network stoichiometry but its experimental flux was removed from the available flux data

Dephosphorylation of β-Arrestin 1 in Glioblastomas

β-Arrestins act as signal terminators for G protein-coupled receptors; they have also been implicated as scaffolding proteins for Src and mitogen-activated protein kinase signaling pathways and transactivators of receptor tyrosine kinases, suggesting their possible role in development and oncogenic signaling. Dephosphorylation of serine 412 is necessary for Src and mitogen-activated protein kinase transactivation. We hypothesized that altered β-arrestin 1 phosphorylation and activation status could play a role in gliomagenesis. Using monoclonal anti-phospho-(serine 412)- and total β-arrestin 1 antibodies, we performed immunohistochemistry on 126 human glioma samples and 7 nonneoplastic controls and Western blot analysis on 5 glioblastomas and 5 nonneoplastic controls. We found high constitutive β-arrestin 1 phosphorylation in nonneoplastic brain tissue, particularly in neurons and neuropil. Most Grade II and III gliomas retained high β-arrestin 1 phosphorylation. By contrast, most of the glioblastoma samples (58/81) showed nearly complete β-arrestin 1 dephosphorylation by immunohistochemistry and decreased relative phosphorylation by Western blot. Expression of constitutively activated epidermal growth factor receptor vIII in U251 cells caused decreased β-arrestin 1 phosphorylation without altering total β-arrestin 1 levels. These results suggest that β-arrestin 1 dephosphorylation/inactivation is associated with aspects of the malignant behavior of glioblastomas

Expression States for the Prototypic TRS for the Environment in Which All Metabolites Are Present as well as the Environment in Which All Metabolites Are Absent

<div><p>The expression (i.e., functional) states for two different environments, as generated by extreme pathway analysis of <b>R*</b>, are presented.</p><p>(A) The expression states for the environment in which all metabolites are present are shown. In this environment, a total of 42 extreme pathways exist leading to the activation or inactivation of the genes within the TRS.</p><p>(B) The expression states for the environment in which all metabolites are absent are shown. In this environment, a total of 62 extreme pathways exist leading to the activation or inactivation of the genes within the TRS. As a legend, triangles represent extracellular cues, namely the six metabolites; squares represent the genes; and circles represent the protein products. Gray elements denote inactivity, whereas colored elements denote active components of the TRS for the given environment. Green arrows indicate that an upstream metabolite or transcription factor activates gene expression, whereas red lines indicate that the upstream metabolite or transcription factor inhibits gene expression. Lines that join together at dots denote “and” relationships within the Boolean regulatory rules, whereas lines that simply join together denote “or” relationships within the Boolean regulatory rules.</p></div