Javier Carrera

Genetdes: automatic design of transcriptional networks.

MOTIVATION The rational design of biological networks with prescribed functions is limited to gene circuits of a few genes. Larger networks involve complex interactions with many parameters and the use of automated computational tools can be very valuable. We propose a new tool to design transcriptional networks with targeted behavior that could be used to better understand the design principles of genetic circuits. RESULTS We have implemented a Simulated Annealing optimization algorithm that explores throughout the space of transcription networks to obtain a specific behavior. The software outputs a transcriptional network with all the corresponding kinetic parameters in SBML format. We provide examples of transcriptional circuits with logical and oscillatory behaviors. Our tool can also be applied to design networks with multiple external input and output genes. AVAILABILITY The software, a tutorial manual, parameter sets and examples are freely available at http://synth-bio.yi.org/genetdes.html.

Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions

BackgroundUnderstanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth conditions, developmental stages, biotic and abiotic stresses, and a variety of mutant genotypes.ResultsWe show that the A. thaliana regulatory network has the characteristic properties of hierarchical networks. We successfully applied our quantitative network model to predict the full transcriptome of the plant for a set of microarray experiments not included in the training dataset. We also used our model to analyze the robustness in expression levels conferred by network motifs such as the coherent feed-forward loop. In addition, the meta-analysis presented here has allowed us to identify regulatory and robust genetic structures.ConclusionsThese data suggest that A. thaliana has evolved high connectivity in terms of transcriptional regulation among cellular functions involved in response and adaptation to changing environments, while gene networks constitutively expressed or less related to stress response are characterized by a lower connectivity. Taken together, these findings suggest conserved regulatory strategies that have been selected during the evolutionary history of this eukaryote.

Changes in the gene expression profile of Arabidopsis thaliana after infection with Tobacco etch virus

BackgroundTobacco etch potyvirus (TEV) has been extensively used as model system for the study of positive-sense RNA virus infecting plants. TEV ability to infect Arabidopsis thaliana varies among ecotypes. In this study, changes in gene expression of A. thaliana ecotype Ler infected with TEV have been explored using long-oligonucleotide arrays. A. thaliana Ler is a susceptible host that allows systemic movement, although the viral load is low and syndrome induced ranges from asymptomatic to mild. Gene expression profiles were monitored in whole plants 21 days post-inoculation (dpi). Microarrays contained 26,173 protein-coding genes and 87 miRNAs.ResultsExpression analysis identified 1727 genes that displayed significant and consistent changes in expression levels either up or down, in infected plants. Identified TEV-responsive genes encode a diverse array of functional categories that include responses to biotic (such as the systemic acquired resistance pathway and hypersensitive responses) and abiotic stresses (droughtness, salinity, temperature, and wounding). The expression of many different transcription factors was also significantly affected, including members of the R2R3-MYB family and ABA-inducible TFs. In concordance with several other plant and animal viruses, the expression of heat-shock proteins (HSP) was also increased. Finally, we have associated functional GO categories with KEGG biochemical pathways, and found that many of the altered biological functions are controlled by changes in basal metabolism.ConclusionTEV infection significantly impacts a wide array of cellular processes, in particular, stress-response pathways, including the systemic acquired resistance and hypersensitive responses. However, many of the observed alterations may represent a global response to viral infection rather than being specific of TEV.

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens.

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules.

Asmparts: assembly of biological model parts

We propose a new computational tool to produce models of biological systems by assembling models from biological parts. Our software not only takes advantage of modularity, but it also enforces standardisation in part characterisation by considering a model of each part. We have used model parts in SBML to design transcriptional networks. Our software is open source, it works in linux and windows platforms, and it could be used to automatically produce models in a server. Our tool not only facilitates model design, but it will also help to promote the establishment of a registry of model parts.

Computational design of synthetic regulatory networks from a genetic library to characterize the designability of dynamical behaviors

The engineering of synthetic gene networks has mostly relied on the assembly of few characterized regulatory elements using rational design principles. It is of outmost importance to analyze the scalability and limits of such a design workflow. To analyze the design capabilities of libraries of regulatory elements, we have developed the first automated design approach that combines such elements to search the genotype space associated to a given phenotypic behavior. Herein, we calculated the designability of dynamical functions obtained from circuits assembled with a given genetic library. By designing circuits working as amplitude filters, pulse counters and oscillators, we could infer new mechanisms for such behaviors. We also highlighted the hierarchical design and the optimization of the interface between devices. We dissected the functional diversity of a constrained library and we found that even such libraries can provide a rich variety of behaviors. We also found that intrinsic noise slightly reduces the designability of digital circuits, but it increases the designability of oscillators. Finally, we analyzed the robust design as a strategy to counteract the evolvability and noise in gene expression of the engineered circuits within a cellular background, obtaining mechanisms for robustness through non-linear negative feedback loops.

A systems biology approach to the evolution of plant-virus interactions.

Omic approaches to the analysis of plant-virus interactions are becoming increasingly popular. These types of data, in combination with models of interaction networks, will aid in revealing not only host components that are important for the virus life cycle, but also general patterns about the way in which different viruses manipulate host regulation of gene expression for their own benefit and possible mechanisms by which viruses evade host defenses. Here, we review studies identifying host genes regulated by viruses and discuss how these genes integrate in host regulatory and interaction networks, with a particular focus on the physical properties of these networks.

Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate.

Synthetic biology uses modeling to facilitate the design of new genetic constructions. In particular, it is of utmost importance to model the reaction of the cellular chassis when expressing heterologous systems. We constructed a mathematical model for the response of a bacterial cell chassis under heterologous expression. For this, we relied on previous characterization of the growth-rate dependence on cellular resource availability (in this case, DNA and RNA polymerases and ribosomes). Accordingly, we estimated the maximum capacities of the cell for heterologous expression to be 46% of the total RNA and the 33% of the total protein. To experimentally validate our model, we engineered two genetic constructions that involved the constitutive expression of a fluorescent reporter in a vector with a tunable origin of replication. We performed fluorescent measurements using population and single-cell fluorescent measurements. Our model predicted cell growth for several heterologous constructions under five different culture conditions and various plasmid copy numbers with significant accuracy, and confirmed that ribosomes act as the limiting resource. Our study also confirmed that the bacterial response to synthetic gene expression could be understood in terms of the requirement for cellular resources and could be predicted from relevant cellular parameters.

Model-based redesign of global transcription regulation

Synthetic biology aims to the design or redesign of biological systems. In particular, one possible goal could be the rewiring of the transcription regulation network by exchanging the endogenous promoters. To achieve this objective, we have adapted current methods to the inference of a model based on ordinary differential equations that is able to predict the network response after a major change in its topology. Our procedure utilizes microarray data for training. We have experimentally validated our inferred global regulatory model in Escherichia coli by predicting transcriptomic profiles under new perturbations. We have also tested our methodology in silico by providing accurate predictions of the underlying networks from expression data generated with artificial genomes. In addition, we have shown the predictive power of our methodology by obtaining the gene profile in experimental redesigns of the E. coli genome, where rewiring the transcriptional network by means of knockouts of master regulators or by upregulating transcription factors controlled by different promoters. Our approach is compatible with most network inference methods, allowing to explore computationally future genome-wide redesign experiments in synthetic biology.

Optimal viral strategies for bypassing RNA silencing

The RNA silencing pathway constitutes a defence mechanism highly conserved in eukaryotes, especially in plants, where the underlying working principle relies on the repressive action triggered by the intracellular presence of double-stranded RNAs. This immune system performs a post-transcriptional suppression of aberrant mRNAs or viral RNAs by small interfering RNAs (siRNAs) that are directed towards their target in a sequence-specific manner. However, viruses have evolved strategies to escape from silencing surveillance while promoting their own replication. Several viruses encode suppressor proteins that interact with different elements of the RNA silencing pathway and block it. The different suppressors are not phylogenetically nor structurally related and also differ in their mechanism of action. Here, we adopt a model-driven forward-engineering approach to understand the evolution of suppressor proteins and, in particular, why viral suppressors preferentially target some components of the silencing pathway. We analysed three strategies characterized by different design principles: replication in the absence of a suppressor, suppressors targeting the first protein component of the pathway and suppressors targeting the siRNAs. Our results shed light on the question of whether a virus must opt for devoting more time into transcription or into translation and on which would be the optimal step of the silencing pathway to be targeted by suppressors. In addition, we discussed the evolutionary implications of such designing principles.