Laparoscopic Hartmann's reversal has better clinical outcomes compared to open surgery: An international multicenter cohort study involving 502 patients.

Background: Hartmann's procedure (HP) is used in surgical emergencies such as colonic perforation and colonic obstruction. "Temporary" colostomy performed during HP is not always reversed in part due to potential morbidity and mortality associated with reversal. There are several contributing factors for patients requiring a permanent colostomy following HP.

Simultaneous cross-evaluation of heterogeneous datasets via mechanistic simulation.

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence.

Modulation of Intracellular O Concentration in Escherichia coli Strains Using Oxygen Consuming Devices.

The use of cell factories for the production of bulk and value-added compounds is nowadays an advantageous alternative to the traditional petrochemical methods. Nevertheless, the efficiency and productivity of several of these processes can improve with the implementation of micro-oxic or anoxic conditions. In the industrial setting, laccases are appealing catalysts that can oxidize a wide range of substrates and reduce O to HO.

Why Build Whole-Cell Models?

Our ability to build computational models that account for all known gene functions in a cell has increased dramatically. But why build whole-cell models, and how can they best be used? In this forum, we enumerate several areas in which whole-cell modeling can significantly impact research and technology.

An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements.

Automated design of bacterial genome sequences.

Background: Organisms have evolved ways of regulating transcription to better adapt to varying environments. Could the current functional genomics data and models support the possibility of engineering a genome with completely rearranged gene organization while the cell maintains its behavior under environmental challenges? How would we proceed to design a full nucleotide sequence for such genomes?

Results: As a first step towards answering such questions, recent work showed that it is possible to design alternative transcriptomic models showing the same behavior under environmental variations than the wild-type model. A second step would require providing evidence that it is possible to provide a nucleotide sequence for a genome encoding such transcriptional model.

Computational design of host transcription-factors sets whose misregulation mimics the transcriptomic effect of viral infections.

The molecular mechanisms underlying viral pathogenesis are yet poorly understood owed to the large number of factors involved and the complexity of their interactions. Could we identify a minimal set of host transcription factors (TF) whose misregulation would result in the transcriptional profile characteristic of infected cells in absence of the virus? How many of such sets exist? Are all orthogonal or share critical TFs involved in specific biological functions? We have developed a computational methodology that uses a quantitative model of the transcriptional regulatory network (TRN) of Arabidopsis thaliana to explore the landscape of all possible re-engineered TRNs whose transcriptomic profiles mimic those observed in infected plants. We found core sets containing between six and 34 TFs, depending on the virus, whose in silico knockout or overexpression in the TRN resulted in transcriptional profiles that minimally deviate from those observed in infected plants.

Computational design of genomic transcriptional networks with adaptation to varying environments.

Transcriptional profiling has been widely used as a tool for unveiling the coregulations of genes in response to genetic and environmental perturbations. These coregulations have been used, in a few instances, to infer global transcriptional regulatory models. Here, using the large amount of transcriptomic information available for the bacterium Escherichia coli, we seek to understand the design principles determining the regulation of its transcriptome.

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.

Perspectives on the automatic design of regulatory systems for synthetic biology.

Automatic design is based on computational modeling and optimization methods to provide prototype designs to targeted problems in an unsupervised manner. For biological circuits, we need to produce quantitative predictions of cell behavior for a given genotype as consequence of the different molecular interactions. Automatic design techniques aim at solving the inverse problem of finding the sequences of nucleotides that better fit a targeted behavior.

Fine-tuning tomato agronomic properties by computational genome redesign.

Considering cells as biofactories, we aimed to optimize its internal processes by using the same engineering principles that large industries are implementing nowadays: lean manufacturing. We have applied reverse engineering computational methods to transcriptomic, metabolomic and phenomic data obtained from a collection of tomato recombinant inbreed lines to formulate a kinetic and constraint-based model that efficiently describes the cellular metabolism from expression of a minimal core of genes. Based on predicted metabolic profiles, a close association with agronomic and organoleptic properties of the ripe fruit was revealed with high statistical confidence.

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.

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.

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.

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.

Robust dynamical pattern formation from a multifunctional minimal genetic circuit.

Background: A practical problem during the analysis of natural networks is their complexity, thus the use of synthetic circuits would allow to unveil the natural mechanisms of operation. Autocatalytic gene regulatory networks play an important role in shaping the development of multicellular organisms, whereas oscillatory circuits are used to control gene expression under variable environments such as the light-dark cycle.

Results: We propose a new mechanism to generate developmental patterns and oscillations using a minimal number of genes.

Network design meets in silico evolutionary biology.

Cell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary approaches and how they can be applied to engineer synthetic networks.

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

Background: Understanding 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.

Towards the automated engineering of a synthetic genome.

The development of the technology to synthesize new genomes and to introduce them into hosts with inactivated wild-type chromosome opens the door to new horizons in synthetic biology. Here it is of outmost importance to harness the ability of using computational design to predict and optimize a synthetic genome before attempting its synthesis. The methodology to computationally design a genome is based on an optimization that computationally mimics genome evolution.

Modular model-based design for heterologous bioproduction in bacteria.

We review the current status of expression of heterologous systems for bioenergy and bioproduction in bacteria using a model-based approach. As an aim for synthetic biology, it requires mathematical models of genetic modules that could be characterized independently of their context. This fastens the design of metabolic circuits using a combinatorial design approach, where given pathways could be optimized for maximal bioproduction, while being nontoxic for the chassis.

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.

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.

DESHARKY: automatic design of metabolic pathways for optimal cell growth.

Motivation: The biological solution for synthesis or remediation of organic compounds using living organisms, particularly bacteria and yeast, has been promoted because of the cost reduction with respect to the non-living chemical approach. In that way, computational frameworks can profit from the previous knowledge stored in large databases of compounds, enzymes and reactions. In addition, the cell behavior can be studied by modeling the cellular context.

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

Background: Tobacco 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.