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Motivation: The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable. Results: We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest. Our dynamic model links the mRNA transcription rate of the target gene to the activation states of the TFs assuming that these interactions are consistent across multiple experiments and over time. A trans-dimensional Markov Chain Monte Carlo (MCMC) algorithm is used to efficiently sample the regulatory logic under different combinations of parents and rank the estimated models by their posterior probabilities. We demonstrate and compare our methodology with other methods using simulation examples and apply it to a study of transcriptional regulation of selected target genes of Arabidopsis Thaliana from microarray time series data obtained under multiple biotic stresses. We show that our method is able to detect complex regulatory interactions that are consistent under multiple experimental conditions. [ABSTRACT FROM AUTHOR]

Background: Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to researchers. The ReTrOS toolbox (Reconstructing Transcription Open Software) provides MATLAB-based implementations of two related methods, namely ReTrOS-Smooth and ReTrOS-Switch, for reconstructing the temporal transcriptional activity profile of a gene from given mRNA expression time series or protein reporter time series. The methods are based on fitting a differential equation model incorporating the processes of transcription, translation and degradation. Results: The toolbox provides a framework for model fitting along with statistical analyses of the model with a graphical interface and model visualisation. We highlight several applications of the toolbox, including the reconstruction of the temporal cascade of transcriptional activity inferred from mRNA expression data and protein reporter data in the core circadian clock in Arabidopsis thaliana, and how such reconstructed transcription profiles can be used to study the effects of different cell lines and conditions. Conclusions: The ReTrOS toolbox allows users to analyse gene and/or protein expression time series where, with appropriate formulation of prior information about a minimum of kinetic parameters, in particular rates of degradation, users are able to infer timings of changes in transcriptional activity. Data from any organism and obtained from a range of technologies can be used as input due to the flexible and generic nature of the model and implementation. The output from this software provides a useful analysis of time series data and can be incorporated into further modelling approaches or in hypothesis generation. [ABSTRACT FROM AUTHOR]

Biological systems show unbounded capacity for complex behaviors and responses to their environments. This principally arises from their genetic networks. The processes governing transcription, translation, and gene regulation are well understood, as are the mechanisms of network evolution, such as gene duplication and horizontal gene transfer. However, the evolved networks arising from these simple processes are much more difficult to understand, and it is difficult to perform experiments on the evolution of these networks in living organisms because of the timescales involved. We propose a new framework for modeling and investigating the evolution of transcription networks in realistic, varied environments. The model we introduce contains novel, important, and lifelike features that allow the evolution of arbitrarily complex transcription networks. Molecular interactions are not specified; instead they are determined dynamically based on shape, allowing protein function to freely evolve. Transcriptional logic provides a flexible mechanism for defining genetic regulatory activity. Simulations demonstrate a realistic life cycle as an emergent property, and that even in simple environments lifelike and complex regulation mechanisms are evolved, including stable proteins, unstable mRNA, and repressor activity. This study also highlights the importance of using in silico genetics techniques to investigate evolved model robustness. [ABSTRACT FROM AUTHOR]

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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

We describe the use of computational models of evolution of artificial gene regulatory networks to understand the topologies of biological gene regulatory networks. We summarize results from three complementary approaches that explicitly represent biological processes of transcription, translation, metabolism and gene regulation: a fine-grained model that allows detailed molecular interactions, a coarse-grained model that allows rapid evolution of many generations, and a fixed-architecture model that allows for comparison of different hypotheses. In the first two cases, we are able to evolve networks towards the biological fitness objectives of survival and reproduction. A theme that emerges is that the control of cell energy and resources is a major driver of gene network topology and function. This is demonstrated in the fine-grained model with the emergence of biologically realistic mRNA and protein turnover rates that optimize energy usage and cell division time, and the evolution of basic repressor activities; in the fixed architecture model with a negative self-regulating gene evolving major efficiencies in mRNA usage; and in the coarse-grained model by the need for the inclusion of basal gene expression to obtain biologically plausible networks and the emergence of global regulators keeping all cellular systems under negative control. In summary, we demonstrate the value of biologically realistic computer evolution techniques, and the importance of energy and resource management in driving the topology and function of gene regulatory networks.