Transcriptional regulatory network discovery via multiple method integration: application to e. coli K12

Transcriptional regulatory network (TRN) discovery from one method (e.g. microarray analysis, gene ontology, phylogenic similarity) does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. We develop a methodology, TRND, that integrates a preliminary TRN, microarray data, gene ontology and phylogenic similarity to accurately discover TRNs and apply the method to E. coli K12. The approach can easily be extended to include other methodologies. Although gene ontology and phylogenic similarity have been used in the context of gene-gene networks, we show that more information can be extracted when gene-gene scores are transformed to gene-transcription factor (TF) scores using a preliminary TRN. This seems to be preferable over the construction of gene-gene interaction networks in light of the observed fact that gene expression and activity of a TF made of a component encoded by that gene is often out of phase. TRND multi-method integration is found to be facilitated by the use of a Bayesian framework for each method derived from its individual scoring measure and a training set of gene/TF regulatory interactions. The TRNs we construct are in better agreement with microarray data. The number of gene/TF interactions we discover is actually double that of existing networks

A proposed role for all-trans retinal in regulation of rhodopsin regeneration in human rods

AbstractIn order to account for the multi-phasic dynamics of photopigment regeneration in human rods, we developed a new model of the retinoid cycle. We first examined the relative roles of the classical and channeling mechanisms of metarhodopsin decay in establishing these dynamics. We showed that neither of these mechanisms alone, nor a linear combination of the two, can adequately account for the dynamics of rhodopsin regeneration at all bleach levels. Our new model adds novel inhibitory interactions in the cycle of regeneration of rhodopsin that are consistent with the 3D structure of rhodopsin. Our analyses show that the dynamics of human rod photopigment regeneration can be accounted for by end-product regulation of the channeling mechanism where all-trans retinal (tral) inhibits the binding of 11-cis retinal to the opsin.tral complex

Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory

BACKGROUND: Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding. RESULTS: Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented. CONCLUSION: Multiplex time series data can be used for the construction of the network of cellular processes and the calibration of the associated physicochemical parameters. We have demonstrated these concepts in the context of gene regulation understood through the analysis of gene expression microarray time series data. Casting the approach in a probabilistic framework has allowed us to address the uncertainties in gene expression microarray data. Our approach was found to be robust to error in the gene expression microarray data and mistakes in a proposed TRN

Network models in the study of metabolism

The systematic study of the genetic fingerprint (genomics) and the biochemistry (metabolites) that goes with a specific cellular process requires the characterization of all the small molecules that form the profile of metabolites and the associated genes. The metabolome represents the collection of all the metabolites during certain process in an organism. The transcriptome represents the gene expression profile, all the messengers RNA in a defined condition. Then to understand the whole process, the studies of metabolites must be accompanied with studies of the gene expression, hence the metabolome must be accompanied by the transcriptome, so we can identify genes and metabolites whose synthesis is induced by a specific process, an infection or stress. Studies of metabolomics generate an enormous amount of data, then they need mathematical and computational tools to establish the correlations between the biochemical and genetic data, and to build up networks that represent the complex metabolic interactions that occur in each case, using tools like Graph and Networks Theory to elucidate the emergent properties inherent to the complex interactions of the metabolic maps. This paper describes the major mathematical tools that can be used for these studies, with emphasis on a semi-qualitative proposal known as the kinetic structural model

Ortoleva: Simulating cellular dynamics through a coupled transcription, translation, metabolic model

In order to predict cell behavior in response to changes in its surroundings or to modifications of its genetic code, the dynamics of a cell are modeled using equations of metabolism, transport, transcription and translation implemented in the Karyote software. Our methodology accounts for the organelles of eukaryotes and the specialized zones in prokaryotes by dividing the volume of the cell into discrete compartments. Each compartment exchanges mass with others either through membrane transport or with a time delay effect associated with molecular migration. Metabolic and macromolecular reactions take place in user-specified compartments. Coupling among processes are accounted for and multiple scale techniques allow for the computation of processes that occur on a wide range of time scales. Our model is implemented to simulate the evolution of concentrations for a user-specifiable set of molecules and reactions that participate in cellular activity. The underlying equations integrate metabolic, transcription and translation reaction networks and provide a framework for simulating whole cells given a user-specified set of reactions. A rate equation formulation is used to simulate transcription from an input DNA sequence while the resulting mRNA is used via ribosome-mediated polymerization kinetics to accomplish translation. Feedback associated with the creation of species necessary for metabolism by the mRNA and protein synthesis modifies the rates of production of factors (e.g. nucleotides and amino acids) that affect the dynamics of transcription and translation. The concentrations of predicted proteins are compared with time series or steady state experiments. The expression and sequence of the predicted proteins are compared with experimental data via the construction of synthetic tryptic digests and associated mass spectra. We present the mathematical model showing the coupling of transcription, translatio