Quantum combinatorial model of gene expression.
Ontology highlight
ABSTRACT:
SUBMITTER: Grover M
PROVIDER: S-EPMC3569601 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Publications
Quantum combinatorial model of gene expression.
Grover Monendra M Grover Ritu R Singh Rakesh R Kumar Rajesh R Kumar Sundeep S
Bioinformation 20130206 3
We propose that the DNA within the chromatin behaves as a dynamic combinatorial library capable of forming novel structures by reversible processes. We also hypothesize that states within the library may be linked via quantum tunneling. RNA polymerase then could scan these states and the system decoheres to the "appropriate" state. Two ways of sustaining quantum coherence at relevant time scales could be possible, first, screening: the quantum system can be kept isolated from its decohering envi ...[more]
Similar Datasets
Project description:Promoters control the expression of genes in response to one or more transcription factors (TFs). The architecture of a promoter is the arrangement and type of binding sites within it. To understand natural genetic circuits and to design promoters for synthetic biology, it is essential to understand the relationship between promoter function and architecture. We constructed a combinatorial library of random promoter architectures. We characterized 288 promoters in Escherichia coli, each containing up to three inputs from four different TFs. The library design allowed for multiple -10 and -35 boxes, and we observed varied promoter strength over five decades. To further analyze the functional repertoire, we defined a representation of promoter function in terms of regulatory range, logic type, and symmetry. Using these results, we identified heuristic rules for programming gene expression with combinatorial promoters.
| S-EPMC2132448 | biostudies-literature
Project description:Constructing gene circuits that satisfy quantitative performance criteria has been a long-standing challenge in synthetic biology. Here, we show a strategy for optimizing a complex three-gene circuit, a novel proportional miRNA biosensor, using predictive modeling to initiate a search in the phase space of sensor genetic composition. We generate a library of sensor circuits using diverse genetic building blocks in order to access favorable parameter combinations and uncover specific genetic compositions with greatly improved dynamic range. The combination of high-throughput screening data and the data obtained from detailed mechanistic interrogation of a small number of sensors was used to validate the model. The validated model facilitated further experimentation, including biosensor reprogramming and biosensor integration into larger networks, enabling in principle arbitrary logic with miRNA inputs using normal form circuits. The study reveals how model-guided generation of genetic diversity followed by screening and model validation can be successfully applied to optimize performance of complex gene networks without extensive prior knowledge.
| S-EPMC5199127 | biostudies-literature
Project description:Understanding the behavior of basic biomolecular components as parts of larger systems is one of the goals of the developing field of synthetic biology. A multidisciplinary approach, involving mathematical and computational modeling in parallel with experimentation, is often crucial for gaining such insights and improving the efficiency of artificial gene network design. Here we used such an approach and developed a combinatorial promoter design strategy to characterize how the position and multiplicity of tetO(2) operator sites within the GAL1 promoter affect gene expression levels and gene expression noise in Saccharomyces cerevisiae. We observed stronger transcriptional repression and higher gene expression noise as a single operator site was moved closer to the TATA box, whereas for multiple operator-containing promoters, we found that the position and number of operator sites together determined the dose-response curve and gene expression noise. We developed a generic computational model that captured the experimentally observed differences for each of the promoters, and more detailed models to successively predict the behavior of multiple operator-containing promoters from single operator-containing promoters. Our results suggest that the independent binding of single repressors is not sufficient to explain the more complex behavior of the multiple operator-containing promoters. Taken together, our findings highlight the importance of joint experimental-computational efforts and some of the challenges of using a bottom-up approach based on well characterized, isolated biomolecular components for predicting the behavior of complex, synthetic gene networks, e.g., the whole can be different from the sum of its parts.
| S-EPMC1931564 | biostudies-literature
Project description:Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.
| S-EPMC6525321 | biostudies-literature
Project description:OBJECTIVE:The specification and differentiation of hematopoietic stem cells into red blood cells requires precise coordination by multiple transcription factors. Most genes important for erythroid maturation are regulated by the Gata family of DNA-binding proteins. Previously, we identified three novel genes kelch-repeat containing protein (krcp), kiaa0650, and testhymin/glucocorticoid inducible transcript 1 (glcci1) to be expressed in erythroid cells in a Gata-independent manner, and we sought to further understand how these transcripts are regulated during zebrafish hematopoiesis. MATERIALS AND METHODS:We employed a loss-of-function approach, using combinations of antisense morpholinos to hematopoietic transcription factors and assayed for changes in gene expression in zebrafish embryos. RESULTS:Upon examination of embryos deficient for Gata1, Gata2, Biklf, and/or Scl, we found distinct gene combinations were required for expression of the novel genes. While krcp expression was dependent upon Gata1 and Biklf, kiaa0650 expression was greatly reduced and glcci1 was maintained in Gata1/Gata2/Biklf-deficient embryos. As with the gata1 gene, kiaa0650 and krcp required Scl for blood expression. Although reduced, glcci1 was expressed in posterior blood precursors in the absence of Scl and Gata2. CONCLUSIONS:This work identifies glcci1 as having Scl-independent expression in the posterior hematopoietic mesoderm, suggesting that its posterior expression is activated by factors upstream or parallel to Scl and Gata2. Additionally, these studies establish that blood gene expression programs are regulated by transcription factors acting in combination during erythroid maturation.
| S-EPMC2390894 | biostudies-literature
Project description:The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors.Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines.LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics.The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized explanation of the reasons for that prognosis for each patient). Moreover, the LAD model provides valuable insights into the roles of individual and combinatorial biomarkers, allows the discovery of new classes of patients, and generates a vast library of biomedical research hypotheses.
| S-EPMC1779471 | biostudies-literature
Project description:There is a growing demand for highly parallel gene expression analysis with whole genome coverage, high sensitivity and high accuracy. Open systems such as differential display are capable of analyzing most of the expressed genome but are not quantitative and generally require manual identification of differentially expressed genes by sequencing. Closed systems such as microarrays use gene-specific probes and are, therefore, limited to studying specific genes in well-characterized species. Here, we describe Tangerine, a PCR-based system that combines the scope and generality of open systems with a robust and immediate identification algorithm using publicly available sequence information. By combinatorial analysis of three independent and complete DNA indexing profiles, each displaying the complete set of expressed transcripts on capillary electrophoresis, the method allows transcripts to be simultaneously quantified and identified. The method is sensitive, accurate and reproducible, and is amenable to high-throughput automated operation.
| S-EPMC519127 | biostudies-literature
Project description:BackgroundDeficiency of the transcription factor MafB, which is normally expressed in macrophages, can underlie cellular dysfunction associated with a range of autoimmune diseases and arteriosclerosis. MafB has important roles in cell differentiation and regulation of target gene expression; however, the mechanisms of this regulation and the identities of other transcription factors with which MafB interacts remain uncertain. Bioinformatics methods provide a valuable approach for elucidating the nature of these interactions with transcriptional regulatory elements from a large number of DNA sequences. In particular, identification of patterns of co-occurrence of regulatory cis-elements (motifs) offers a robust approach.ResultsHere, the directional relationships among several functional motifs were evaluated using the Log-linear Graphical Model (LGM) after extraction and search for evolutionarily conserved motifs. This analysis highlighted GATA-1 motifs and 5'AT-rich half Maf recognition elements (MAREs) in promoter regions of 18 genes that were down-regulated in Mafb deficient macrophages. GATA-1 motifs and MafB motifs could regulate expression of these genes in both a negative and positive manner, respectively. The validity of this conclusion was tested with data from a luciferase assay that used a C1qa promoter construct carrying both the GATA-1 motifs and MAREs. GATA-1 was found to inhibit the activity of the C1qa promoter with the GATA-1 motifs and MafB motifs.ConclusionsThese observations suggest that both the GATA-1 motifs and MafB motifs are important for lineage specific expression of C1qa. In addition, these findings show that analysis of combinations of evolutionarily conserved motifs can be successfully used to identify patterns of gene regulation.
| S-EPMC3287487 | biostudies-literature
Project description:Vascular development begins when mesodermal cells differentiate into endothelial cells, which then form primitive vessels. It has been hypothesized that endothelial-specific gene expression may be regulated combinatorially, but the transcriptional mechanisms governing specificity in vascular gene expression remain incompletely understood. Here, we identify a 44 bp transcriptional enhancer that is sufficient to direct expression specifically and exclusively to the developing vascular endothelium. This enhancer is regulated by a composite cis-acting element, the FOX:ETS motif, which is bound and synergistically activated by Forkhead and Ets transcription factors. We demonstrate that coexpression of the Forkhead protein FoxC2 and the Ets protein Etv2 induces ectopic expression of vascular genes in Xenopus embryos, and that combinatorial knockdown of the orthologous genes in zebrafish embryos disrupts vascular development. Finally, we show that FOX:ETS motifs are present in many known endothelial-specific enhancers and that this motif is an efficient predictor of endothelial enhancers in the human genome.
| S-EPMC2782666 | biostudies-literature
Project description:BackgroundThe progress in mapping RNA-protein and RNA-RNA interactions at the transcriptome-wide level paves the way to decipher possible combinatorial patterns embedded in post-transcriptional regulation of gene expression.ResultsHere we propose an innovative computational tool to extract clusters of mRNA trans-acting co-regulators (RNA binding proteins and non-coding RNAs) from pairwise interaction annotations. In addition the tool allows to analyze the binding site similarity of co-regulators belonging to the same cluster, given their positional binding information. The tool has been tested on experimental collections of human and yeast interactions, identifying modules that coordinate functionally related messages.ConclusionsThis tool is an original attempt to uncover combinatorial patterns using all the post-transcriptional interaction data available so far. PTRcombiner is available at http://disi.unitn.it/~passerini/software/PTRcombiner/.
| S-EPMC4234518 | biostudies-literature