Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface

Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment

Genome-wide analysis of the role of the antibiotic biosynthesis regulator AbsA2 in Streptomyces coelicolor A3(2)

<div><p>The AbsA1-AbsA2 two component signalling system of <i>Streptomyces coelicolor</i> has long been known to exert a powerful negative influence on the production of the antibiotics actinorhodin, undecylprodiginine and the Calcium-Dependent Antibiotic (CDA). Here we report the analysis of a <i>ΔabsA2</i> deletion strain, which exhibits the classic precocious antibiotic hyper-production phenotype, and its complementation by an N-terminal triple-FLAG-tagged version of AbsA2. The complemented and non-complemented <i>ΔabsA2</i> mutant strains were used in large-scale microarray-based time-course experiments to investigate the effect of deleting <i>absA2</i> on gene expression and to identify the <i>in vivo</i> AbsA2 DNA-binding target sites using ChIP-on chip. We show that in addition to binding to the promoter regions of <i>redZ</i> and <i>actII-orfIV</i> AbsA2 binds to several previously unidentified sites within the <i>cda</i> biosynthetic gene cluster within and/or upstream of <i>SCO3215—SCO3216</i>, <i>SCO3217</i>, <i>SCO3229—SCO3230</i>, and <i>SCO3226</i>, and we relate the pattern of AbsA2 binding to the results of the transcriptomic study and antibiotic phenotypic assays. Interestingly, dual ‘biphasic’ ChIP peaks were observed with AbsA2 binding across the regulatory genes <i>actII-orfIV</i> and <i>redZ</i> and the <i>absA2</i> gene itself, while more conventional single promoter-proximal peaks were seen at the CDA biosynthetic genes suggesting a different mechanism of regulation of the former loci. Taken together the results shed light on the complex mechanism of regulation of antibiotic biosynthesis in <i>Streptomyces coelicolor</i> and the important role of AbsA2 in controlling the expression of three antibiotic biosynthetic gene clusters.</p></div

Strong negative self regulation of Prokaryotic transcription factors increases the intrinsic noise of protein expression

Background Many prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques. Results We undertake a novel analysis of a classic model for negative self regulation. We demonstrate that, with standard approximations, protein variance relative to its mean should be independent of repressor strength in a physiological range. Consequently, in that range, the coefficient of variation would increase with repressor strength. However, stochastic computer simulations demonstrate that there is a greater increase in noise associated with strong repressors than predicted by theory. The discrepancies between the mathematical analysis and computer simulations arise because with strong repressors the approximation that leads to Michaelis-Menten-like hyperbolic repression terms ceases to be valid. Because we observe that strong negative feedback increases variability and so is unlikely to be a mechanism for noise control, we suggest instead that negative feedback is evolutionarily favoured because it allows the cell to minimize mRNA usage. To test this, we used in silico evolution to demonstrate that while negative feedback can achieve only a modest improvement in protein noise reduction compared with the unregulated system, it can achieve good improvement in protein response times and very substantial improvement in reducing mRNA levels. Conclusions Strong negative self regulation of transcription may not always be a mechanism for homeostatic control of protein abundance, but instead might be evolutionarily favoured as a mechanism to limit the use of mRNA. The use of hyperbolic terms derived from quasi-steady-state approximation should also be avoided in the analysis of stochastic models with strong repressors

The 3′ Splice Site of Influenza A Segment 7 mRNA Can Exist in Two Conformations: A Pseudoknot and a Hairpin

The 3′ splice site of influenza A segment 7 is used to produce mRNA for the M2 ion-channel protein, which is critical to the formation of viable influenza virions. Native gel analysis, enzymatic/chemical structure probing, and oligonucleotide binding studies of a 63 nt fragment, containing the 3′ splice site, key residues of an SF2/ASF splicing factor binding site, and a polypyrimidine tract, provide evidence for an equilibrium between pseudoknot and hairpin structures. This equilibrium is sensitive to multivalent cations, and can be forced towards the pseudoknot by addition of 5 mM cobalt hexammine. In the two conformations, the splice site and other functional elements exist in very different structural environments. In particular, the splice site is sequestered in the middle of a double helix in the pseudoknot conformation, while in the hairpin it resides in a two-by-two nucleotide internal loop. The results suggest that segment 7 mRNA splicing can be controlled by a conformational switch that exposes or hides the splice site

Comparisons between Chemical Mapping and Binding to Isoenergetic Oligonucleotide Microarrays Reveal Unexpected Patterns of Binding to the Bacillus subtilis RNase P RNA Specificity Domain†

ABSTRACT: Microarrays with isoenergetic pentamer and hexamer 20-O-methyl oligonucleotide probes with LNA (locked nucleic acid) and 2,6-diaminopurine substitutions were used to probe the binding sites on theRNase P RNA specificity domain of Bacillus subtilis. Unexpected binding patterns were revealed. Because of their enhanced binding free energies, isoenergetic probes can break short duplexes, merge adjacent loops, and/or induce refolding. This suggests new approaches to the rational design of short oligonucleotide therapeutics but limits the utility of microarrays for providing constraints for RNA structure determination. The microarray results are compared to results from chemical mapping experiments, which do provide constraints. Results from both types of experiments indicate that the RNase P RNA folds similarly in 1MNaþ and 10 mMMg2þ. Binding of RNA to RNA is important for many natural func-tions, includingproteinsynthesis (1,2), translationregulation (3,4), gene silencing (5, 6), metabolic regulation (7), RNAmodification (8, 9), etc. (10-13). Binding of oligonucleotides toRNAs is impor-tant for therapeutic approaches, such as siRNA, ribozymes, and antisense therapy (14, 15).Much remains to bediscovered, however, of the rules for predicting binding sites andpotential therapeutics

Equation-Free Analysis of Two-Component System Signalling Model Reveals the Emergence of Co-Existing Phenotypes in the Absence of Multistationarity

Phenotypic differences of genetically identical cells under the same environmental conditions have been attributed to the inherent stochasticity of biochemical processes. Various mechanisms have been suggested, including the existence of alternative steady states in regulatory networks that are reached by means of stochastic fluctuations, long transient excursions from a stable state to an unstable excited state, and the switching on and off of a reaction network according to the availability of a constituent chemical species. Here we analyse a detailed stochastic kinetic model of two-component system signalling in bacteria, and show that alternative phenotypes emerge in the absence of these features. We perform a bifurcation analysis of deterministic reaction rate equations derived from the model, and find that they cannot reproduce the whole range of qualitative responses to external signals demonstrated by direct stochastic simulations. In particular, the mixed mode, where stochastic switching and a graded response are seen simultaneously, is absent. However, probabilistic and equation-free analyses of the stochastic model that calculate stationary states for the mean of an ensemble of stochastic trajectories reveal that slow transcription of either response regulator or histidine kinase leads to the coexistence of an approximate basal solution and a graded response that combine to produce the mixed mode, thus establishing its essential stochastic nature. The same techniques also show that stochasticity results in the observation of an all-or-none bistable response over a much wider range of external signals than would be expected on deterministic grounds. Thus we demonstrate the application of numerical equation-free methods to a detailed biochemical reaction network model, and show that it can provide new insight into the role of stochasticity in the emergence of phenotypic diversity

Thermodynamic contributions of single internal rA·dA, rC·dC, rG·dG and rU·dT mismatches in RNA/DNA duplexes

The thermodynamic contributions of rA·dA, rC·dC, rG·dG and rU·dT single internal mismatches were measured for 54 RNA/DNA duplexes in a 1 M NaCl buffer using UV absorbance thermal denaturation. Thermodynamic parameters were obtained by fitting absorbance versus temperature profiles using the curve-fitting program Meltwin. The weighted average thermodynamic data were fit using singular value decomposition to determine the eight non-unique nearest-neighbor parameters for each internal mismatch. The new parameters predict the ΔG°37, ΔH° and melting temperature (Tm) of duplexes containing these single mismatches within an average of 0.33 kcal/mol, 4.5 kcal/mol and 1.4°C, respectively. The general trend in decreasing stability for the single internal mismatches is rG·dG > rU·dT > rA·dA > rC·dC. The stability trend for the base pairs 5′ of the single internal mismatch is rG·dC > rC·dG > rA·dT > rU·dA. The stability trend for the base pairs 3′ of the single internal mismatch is rC·dG > rG·dC >> rA·dT > rU·dA. These nearest-neighbor values are now a part of a complete set of single internal mismatch thermodynamic parameters for RNA/DNA duplexes that are incorporated into the nucleic acid assay development software programs Visual oligonucleotide modeling platform (OMP) and ThermoBLAST

Nonidentifiability of the Source of Intrinsic Noise in Gene Expression from Single-Burst Data

Over the last few years, experimental data on the fluctuations in gene activity between individual cells and within the same cell over time have confirmed that gene expression is a “noisy” process. This variation is in part due to the small number of molecules taking part in some of the key reactions that are involved in gene expression. One of the consequences of this is that protein production often occurs in bursts, each due to a single promoter or transcription factor binding event. Recently, the distribution of the number of proteins produced in such bursts has been experimentally measured, offering a unique opportunity to study the relative importance of different sources of noise in gene expression. Here, we provide a derivation of the theoretical probability distribution of these bursts for a wide variety of different models of gene expression. We show that there is a good fit between our theoretical distribution and that obtained from two different published experimental datasets. We then prove that, irrespective of the details of the model, the burst size distribution is always geometric and hence determined by a single parameter. Many different combinations of the biochemical rates for the constituent reactions of both transcription and translation will therefore lead to the same experimentally observed burst size distribution. It is thus impossible to identify different sources of fluctuations purely from protein burst size data or to use such data to estimate all of the model parameters. We explore methods of inferring these values when additional types of experimental data are available