Biomedical subjects

Anirvan M Sengupta

Publications and source records attributed to Anirvan M Sengupta.

9 recordsLinked to original sources

PubMed2006

Thermodynamic and kinetic modeling of transcriptional pausing.

We present a statistical mechanics approach for the prediction of backtracked pauses in bacterial transcription elongation derived from structural models of the transcription elongation complex (EC). Our algorithm is based on the thermodynamic stability of the EC along the DNA template calculated from the sequence-dependent free energy of DNA-DNA, DNA-RNA, and RNA-RNA base pairing associated with (i) the translocational and size fluctuations of the transcription bubble; (ii) changes in the associated DNA-RNA hybrid; and (iii) changes in the cotranscriptional RNA secondary structure upstream of the RNA exit channel. The calculations involve no adjustable parameters except for a cutoff used to discriminate paused from nonpaused complexes. When applied to 100 experimental pauses in transcription elongation by Escherichia coli RNA polymerase on 10 DNA templates, the approach produces statistically significant results. We also present a kinetic model for the rate of recovery of backtracked paused complexes. A crucial ingredient of our model is the incorporation of kinetic barriers to backtracking resulting from steric clashes of EC with the cotranscriptionally generated RNA secondary structure, an aspect not included explicitly in previous attempts at modeling the transcription elongation process.

Computational Biology↗

PubMed2005

Quantitative modeling and data analysis of SELEX experiments.

SELEX (systematic evolution of ligands by exponential enrichment) is an experimental procedure that allows the extraction, from an initially random pool of DNA, of those oligomers with high affinity for a given DNA-binding protein. We address what is a suitable experimental and computational procedure to infer parameters of transcription factor-DNA interaction from SELEX experiments. To answer this, we use a biophysical model of transcription factor-DNA interactions to quantitatively model SELEX. We show that a standard procedure is unsuitable for obtaining accurate interaction parameters. However, we theoretically show that a modified experiment in which chemical potential is fixed through different rounds of the experiment allows robust generation of an appropriate dataset. Based on our quantitative model, we propose a novel bioinformatic method of data analysis for such a modified experiment and apply it to extract the interaction parameters for a mammalian transcription factor CTF/NFI. From a practical point of view, our method results in a significantly improved false positive/false negative trade-off, as compared to both the standard information theory based method and a widely used empirically formulated procedure.

Animals↗

PubMed2005

Stochastic evolutionary dynamics on two levels.

We consider a population that is subdivided into groups. Individuals reproduce proportional to their fitness. When a group reaches a certain size it has a probability to split into two groups while another group is eliminated. In this stochastic process, the number of groups is constant, while the total population size fluctuates between well-defined bounds. We calculate the fixation probability of newly introduced mutants under constant selection. We show that the described population structure acts as a suppressor of selection compared to an unstructured population of the same size. The maximum suppression of selection is obtained, when the number of groups equals the number of individuals per group. We also study opposing selection on two or more levels by analysing the evolutionary dynamics of hierarchically embedded Moran processes.

Animals↗

PubMed2005

G-protein-coupled enzyme cascades have intrinsic properties that improve signal localization and fidelity.

G-protein-coupled enzyme cascades are used by eukaryotic cells to detect external signals and transduce them into intracellular messages that contain biological information relevant to the cell's function. Since G-protein-coupled receptors that are designed to detect different kinds of external signals can generate the same kind of intracellular response, effective signaling requires that there are mechanisms to increase signal specificity and fidelity. Here we examine the kinetic equations for the initial three stages in a generic G-protein-coupled cascade and show that the physical properties of the transduction pathway result in two intrinsic features that benefit signaling. 1), The response to a single activated receptor is naturally confined to a localized spatial domain, which could improve signal specificity by reducing cross talk. 2), The peak of the response generated by such a signaling domain is limited. This saturation effect reduces trial-to-trial variability and increases signaling fidelity by limiting the response to receptors that remain active for longer than average. We suggest that this mechanism for reducing response fluctuations may be a contributing factor in making the single photon responses of vertebrate retinal rods so remarkably reproducible.

Animals↗

PubMed2005

Optimal path to epigenetic switching.

We use large deviation methods to calculate rates of noise-induced transitions between states in multistable genetic networks. We analyze a synthetic biochemical circuit, the toggle switch, and compare the results to those obtained from a numerical solution of the master equation.

Adaptation, Physiological↗

PubMed2004

Repression of the yeast HO gene by the MATalpha2 and MATa1 homeodomain proteins.

The HO gene in Saccharomyces cerevisiae is regulated by a large and complex promoter that is similar to promoters in higher order eukaryotes. Within this promoter are 10 potential binding sites for the a1-alpha2 heterodimer, which represses HO and other haploid-specific genes in diploid yeast cells. We have determined that a1-alpha2 binds to these sites with differing affinity, and that while certain strong-affinity sites are crucial for repression of HO, some of the weak-affinity sites are dispensable. However, these weak-affinity a1-alpha2-binding sites are strongly conserved in related yeast species and have a role in maintaining repression upon the loss of strong-affinity sites. We found that these weak sites are sufficient for a1-alpha2 to partially repress HO and recruit the Tup1-Cyc8 (Tup1-Ssn6) co-repressor complex to the HO promoter. We demonstrate that the Swi5 activator protein is not bound to URS1 in diploid cells, suggesting that recruitment of the Tup1-Cyc8 complex by a1-alpha2 prevents DNA binding by activator proteins resulting in repression of HO.

Base Sequence↗

PubMed2004

Combined analysis of expression data and transcription factor binding sites in the yeast genome.

BACKGROUND: The analysis of gene expression using DNA microarrays provides genome wide profiles of the genes controlled by the presence or absence of a specific transcription factor. However, the question arises of whether a change in the level of transcription of a specific gene is caused by the transcription factor acting directly at the promoter of the gene or through regulation of other transcription factors working at the promoter. RESULTS: To address this problem we have devised a computational method that combines microarray expression and site preference data. We have tested this approach by identifying functional targets of the a1-alpha2 complex, which represses haploid-specific genes in the yeast Saccharomyces cerevisiae. Our analysis identified many known or suspected haploid-specific genes that are direct targets of the a1-alpha2 complex, as well as a number of previously uncharacterized targets. We were also able to identify a number of haploid-specific genes which do not appear to be direct targets of the a1-alpha2 complex, as well as a1-alpha2 target sites that do not repress transcription of nearby genes. Our method has a much lower false positive rate when compared to some of the conventional bioinformatic approaches. CONCLUSIONS: These findings show advantages of combining these two forms of data to investigate the mechanism of co-regulation of specific sets of genes.

Algorithms↗

PubMed2003

A biophysical approach to transcription factor binding site discovery.

Identification of transcription factor binding sites within regulatory segments of genomic DNA is an important step toward understanding of the regulatory circuits that control expression of genes. Here, we describe a novel bioinformatics method that bases classification of potential binding sites explicitly on the estimate of sequence-specific binding energy of a given transcription factor. The method also estimates the chemical potential of the factor that defines the threshold of binding. In contrast with the widely used information-theoretic weight matrix method, the new approach correctly describes saturation in the transcription factor/DNA binding probability. This results in a significant improvement in the number of expected false positives, particularly in the ubiquitous case of low-specificity factors. In the strong binding limit, the algorithm is related to the "support vector machine" approach to pattern recognition. The new method is used to identify likely genomic binding sites for the E. coli transcription factors collected in the DPInteract database. In addition, for CRP (a global regulatory factor), the likely regulatory modality (i.e., repressor or activator) of predicted binding sites is determined.

Algorithms↗

PubMed2002

Specificity and robustness in transcription control networks.

Recognition by transcription factors of the regulatory DNA elements upstream of genes is the fundamental step in controlling gene expression. How does the necessity to provide stability with respect to mutation constrain the organization of transcription control networks? We examine the mutation load of a transcription factor interacting with a set of n regulatory response elements as a function of the factor/DNA binding specificity and conclude on theoretical grounds that the optimal specificity decreases with n. The predicted correlation between variability of binding sites (for a given transcription factor) and their number is supported by the genomic data for Escherichia coli. The analysis of E. coli genomic data was carried out using an algorithm suggested by the biophysical model of transcription factor/DNA binding. Complete results of the search for candidate transcription factor binding sites are available at http://www.physics.rockefeller.edu/~boris/public/search_ecoli.

Algorithms↗