The construction of transcription factor networks through natural selection

Transcription regulation plays a key role in determining cellular function, response to external stimuli and development. Regulatory proteins orchestrate gene expression through thousands of interactions resulting in large, complex networks. Understanding the principles on which these networks are constructed can provide insight into the way the expression patterns of different genes co-evolve. One method by which this question can be addressed is to focus on the evolution of the structure of transcription factor networks (TFNs). In order to do this, a model for their evolution through cis mutation, trans mutation, gene duplication and gene deletion is constructed. This model is used to determine the circumstances under which the asymmetrical in and out degree distributions observed in real networks are reproduced. In this way it is possible to draw conclusions about the contributions of these different evolutionary processes to the evolution of TFNs. Conclusions are also drawn on the way rates of evolution vary with the position of gene in the network. Following this, the contributions of cis mutations, which occur in the promoters of regulated genes, and trans mutations, which occur in the coding reign of transcription factors, to the evolution of TFNs are investigated. A space of neutral genotypes is constructed, and the evolution of TFNs through cis and trans mutations in this space is characterised. The results are then used to account for large scale rewiring observed in the yeast sex determination network. Finally the principles governing the evolution of autoregulatory motifs are investigated. It is shown that negative autoregulation, which functions as a noise reduction mechanism in haploid TFNs, is not evolvable in diploid TFNs. This is attributed to the effects of dominance in diploid TFNs. The fate of duplicates of autoregulating genes in haploid networks is also investigated. It is shown that such duplicates are especially prone to loss of function mutations. This is used to account for the lack of observed autoregulatory duplicates participating in network motifs. From this work, it is concluded that the relative rates of different evolutionary processes are responsible for shaping the global statistical properties of TFN structure. However, the more detailed TFN structure, such as network motif distribution, is strongly influenced by the population genetic details of the system being considered. In addition, extensive neutral evolution is shown to be possible in TFNs. However, the effects of neutral evolution on network structure are shown to depend strongly on the structure of the space on neutral genotypes in which the TFN is evolving

An information-flow-based model with dissipation, saturation and direction for active pathway inference

<p>Abstract</p> <p>Background</p> <p>Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow.</p> <p>Results</p> <p>In this study, we propose new concepts of dissipation, saturation and direction to decipher the information flow behaviours in the pathways and thereby infer the biological pathways from a given source to its target. This model takes into account explicitly the common features of the information transmission and provides a general framework to model the biological pathways. It can incorporate different types of bio-molecular interactions to infer the signal transduction pathways and interpret the expression quantitative trait loci (eQTL) associations. The model is formulated as a linear programming problem and thus is solved efficiently. Experiments on the real data of yeast indicate that the reproduced pathways are highly consistent with the current knowledge.</p> <p>Conclusions</p> <p>Our model explicitly treats the biological pathways as information flows with dissipation, saturation and direction. The effective applications suggest that the three new concepts may be valid to describe the organization rules of biological pathways. The deduced linear programming should be a promising tool to infer the various biological pathways from the high-throughput data.</p

Swimming Upstream: Identifying Proteomic Signals that Drive Transcriptional Changes using the Interactome and Multiple “-Omics” Datasets

available in PMC 2013 December 23Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, “-omics” methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differentially expressed transcripts identified in the experiments “constraints” using previously reported protein–protein and protein–DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions “optimization”. A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here, we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.National Cancer Institute (U.S.) ((NCI) Grant U54-CA112967)Natural Sciences and Engineering Research Council of Canada (Postgraduate scholarship)Massachusetts Institute of Technology (Eugene Bell Career Development Chair)National Cancer Institute (U.S.) (NCI integrative cancer biology program graduate fellowship

Dynamically Reshaping Signaling Networks to Program Cell Fate via Genetic Controllers

Introduction: Engineering of cell fate through synthetic gene circuits requires methods to precisely implement control around native decision-making pathways and offers the potential to direct developmental programs and redirect aberrantly activated cell processes. We set out to develop molecular network diverters, a class of genetic control systems, to activate or attenuate signaling through a mitogen-activated protein kinase (MAPK) pathway, the yeast mating pathway, to conditionally route cells to one of three distinct fates. Methods: We used a combination of genetic elements—including pathway regulators, RNA-based transducers, and constitutive and pathway-responsive promoters—to build modular network diverters. We measured the impact of these genetic control systems on pathway activity by monitoring fluorescence from a transcriptional pathway reporter. Cell fate determination was measured through halo assays, in which mating-associated cell cycle arrest above a certain concentration of pheromone from wild-type cells results in a “halo” or cleared region around a disk saturated in pheromone. A phenomenological model of our system was built to elucidate design principles for dual diverters that integrate opposing functions while supporting independent routing to alternative fates. Results: We identified titratable positive (Ste4) and negative (Msg5) regulators of pathway activity that result in divergent cell fate decisions when controlled from network diverters. A positive diverter, controlling Ste4 through a feedback architecture, routed cells to the mating fate, characterized by pathway activation in the absence of pheromone. A negative diverter, controlling Msg5 through a nonfeedback architecture, routed cells to the nonmating fate, characterized by pathway inhibition in the presence of pheromone. When integrated into a dual-diverter architecture, the opposing functions of these positive and negative diverters resulted in antagonism, which prevented independent routing to the alternative fates. However, a modified architecture that incorporated both constitutive and feedback regulation over the pathway regulators enabled conditional routing of cells to one of three fates (wild type, mating, or nonmating) in response to specified environmental signals. Discussion: Our work identified design principles for networks that induce differentiation of cells in response to environmental signals and that enhance the robust performance of integrated mutually antagonistic genetic programs. For example, integrated negative regulators can buffer a system against noise amplification mediated through positive-feedback loops by providing a resistance to amplification. Negative feedback can play an important role by reducing population heterogeneity and mediating robust, long-term cell fate decisions. The dual-diverter configuration enables routing to alternative fates and minimizes impact on the opposing diverter by integrating differential regulatory strategies on functionally redundant genes. Molecular network diverters provide a foundation for robustly programming spatial and temporal control over cell fate

A focus on yeast mating: From pheromone signaling to cell-cell fusion.

Cells live in a chemical environment and are able to orient towards chemical cues. Unicellular haploid fungal cells communicate by secreting pheromones to reproduce sexually. In the yeast models Saccharomyces cerevisiae and Schizosaccharomyces pombe, pheromonal communication activates similar pathways composed of cognate G-protein-coupled receptors and downstream small GTPase Cdc42 and MAP kinase cascades. Local pheromone release and sensing, at a mobile surface polarity patch, underlie spatial gradient interpretation to form pairs between two cells of distinct mating types. Concentration of secretion at the point of cell-cell contact then leads to local cell wall digestion for cell fusion, forming a diploid zygote that prevents further fusion attempts. A number of asymmetries between mating types may promote efficiency of the system. In this review, we present our current knowledge of pheromone signaling in the two model yeasts, with an emphasis on how cells decode the pheromone signal spatially and ultimately fuse together. Though overall pathway architectures are similar in the two species, their large evolutionary distance allows to explore how conceptually similar solutions to a general biological problem can arise from divergent molecular components

Systems Level Modeling of the Cell Cycle Using Budding Yeast

Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism

Using movies to analyse gene circuit dynamics in single cells

Many bacterial systems rely on dynamic genetic circuits to control crucial biological processes. A major goal of systems biology is to understand these behaviours in terms of individual genes and their interactions. However, traditional techniques based on population averages 'wash out' crucial dynamics that are either unsynchronized between cells or are driven by fluctuations, or 'noise', in cellular components. Recently, the combination of time-lapse microscopy, quantitative image analysis and fluorescent protein reporters has enabled direct observation of multiple cellular components over time in individual cells. In conjunction with mathematical modelling, these techniques are now providing powerful insights into genetic circuit behaviour in diverse microbial systems

Hsp90 orchestrates transcriptional regulation by Hsf1 and cell wall remodelling by MAPK signalling during thermal adaptation in a pathogenic yeast

Acknowledgments We thank Rebecca Shapiro for creating CaLC1819, CaLC1855 and CaLC1875, Gillian Milne for help with EM, Aaron Mitchell for generously providing the transposon insertion mutant library, Jesus Pla for generously providing the hog1 hst7 mutant, and Cathy Collins for technical assistance.Peer reviewedPublisher PD