Operator Sequence Alters Gene Expression Independently of Transcription Factor Occupancy in Bacteria

A canonical quantitative view of transcriptional regulation holds that the only role of operator sequence is to set the probability of transcription factor binding, with operator occupancy determining the level of gene expression. In this work, we test this idea by characterizing repression in vivo and the binding of RNA polymerase in vitro in experiments where operators of various sequences were placed either upstream or downstream from the promoter in Escherichia coli. Surprisingly, we find that operators with a weaker binding affinity can yield higher repression levels than stronger operators. Repressor bound to upstream operators modulates promoter escape, and the magnitude of this modulation is not correlated with the repressor-operator binding affinity. This suggests that operator sequences may modulate transcription by altering the nature of the interaction of the bound transcription factor with the transcriptional machinery, implying a new layer of sequence dependence that must be confronted in the quantitative understanding of gene expression

Representing and analysing molecular and cellular function in the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model

Synthesizing and tuning chemical reaction networks with specified behaviours

We consider how to generate chemical reaction networks (CRNs) from functional specifications. We propose a two-stage approach that combines synthesis by satisfiability modulo theories and Markov chain Monte Carlo based optimisation. First, we identify candidate CRNs that have the possibility to produce correct computations for a given finite set of inputs. We then optimise the reaction rates of each CRN using a combination of stochastic search techniques applied to the chemical master equation, simultaneously improving the of correct behaviour and ruling out spurious solutions. In addition, we use techniques from continuous time Markov chain theory to study the expected termination time for each CRN. We illustrate our approach by identifying CRNs for majority decision-making and division computation, which includes the identification of both known and unknown networks.Comment: 17 pages, 6 figures, appeared the proceedings of the 21st conference on DNA Computing and Molecular Programming, 201

Computer simulation of pulsed field gel runs allows the quantitation of radiation-induced double-strand breaks in yeast

A procedure for the quantification of double-strand breaks in yeast is presented that utilizes pulsed field gel electrophoresis (PFGE) and a comparison of the observed DNA mass distribution in the gel lanes with calculated distributions. Calculation of profiles is performed as follows. If double-strand breaks are produced by sparsely ionizing radiation, one can assume that they are distributed randomly in the genome, and the resulting DNA mass distribution in molecular length can be predicted by means of a random breakage model. The input data for the computation of molecular length profiles are the breakage frequency per unit length, , as adjustable parameter, and the molecular lengths of the intact chromosomes. The obtained DNA mass distributions in molecular length must then be transformed into distributions of DNA mass in migration distance. This requires a calibration of molecular length vs. migration distance that is specific for the gel lane in question. The computed profiles are then folded with a Lorentz distribution with adjusted spread parameter to account for and broadening. The DNA profiles are calculated for different breakage frequencies and for different values of , and the parameters resulting in the best fit of the calculated to the observed profile are determined

A Genetic Locus Regulates the Expression of Tissue-Specific mRNAs from Multiple Transcription Units

129 GIX- mice, unlike animals of the congeneic partner strain GIX+, do not express significant amounts of the retroviral antigens gp70 and p30. Evidence is presented indicating that the GIX phenotype is specified by a distinct regulatory gene acting on multiple transcription units to control the levels of accumulation of specific mRNA species. The steady-state levels of retroviral-homologous mRNA from the tissues of GIX+ and GIX- mice were examined by blot hybridization using as probes DNA fragments from cloned murine leukemia viruses. RNA potentially encoding viral antigens was reduced or absent in GIX- mice, even though no differences in integrated viral genomes were detected between these congeneic strains by DNA blotting. Tissue-specific patterns of accumulation of these RNA species were detected in brain, epididymis, liver, spleen, and thymus, and several distinct RNA species were found to be coordinately regulated with the GIX phenotype. Measurements of RNA synthesis suggest a major role for transcriptional control in the regulation of some retroviral messages

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes