Digital publishing isn't enough: the case for 'blueprints' in scientific communication.

Since the time of Newton and Galileo, the tools for capturing and communicating science have remained conceptually unchanged - in essence, they consist of observations on paper (or electronic variants), followed by a 'letter' to the community to report your findings. These age-old tools are inadequate for the complexity of today's scientific challenges. If modern software engineering worked like science, programmers would not share open source code; they would take notes on their work and then publish long-form articles about their software.

Evaluation of a novel cloud-based software platform for structured experiment design and linked data analytics.

Open data in science requires precise definition of experimental procedures used in data generation, but traditional practices for sharing protocols and data cannot provide the required data contextualization. Here, we explore implementation, in an academic research setting, of a novel cloud-based software system designed to address this challenge. The software supports systematic definition of experimental procedures as visual processes, acquisition and analysis of primary data, and linking of data and procedures in machine-computable form.

Rewriting yeast central carbon metabolism for industrial isoprenoid production.

A bio-based economy has the potential to provide sustainable substitutes for petroleum-based products and new chemical building blocks for advanced materials. We previously engineered Saccharomyces cerevisiae for industrial production of the isoprenoid artemisinic acid for use in antimalarial treatments. Adapting these strains for biosynthesis of other isoprenoids such as β-farnesene (CH), a plant sesquiterpene with versatile industrial applications, is straightforward.

Synthetic biology: evolution or revolution? A co-founder's perspective.

In this article, we relate the story of Synthetic Biology's birth, from the perspective of a co-founder, and consider its original premise--that standardization and abstraction of biological components will unlock the full potential of biological engineering. The standardization ideas of Synthetic Biology emerged in the late 1990s from a convergence of research on cellular computing, and were motivated by an array of applications from tissue regeneration to bio-sensing to mathematical programming. As the definition of Synthetic Biology has grown to be synonymous with Biological Engineering and Biotechnology, the field has lost sight of the fact that its founding premise has not yet been validated.

Industrial fermentation of renewable diesel fuels.

In commodity chemicals, cost drives everything. A working class family of four drives up to the gas pumps and faces a choice of a renewable diesel or petroleum diesel. Renewable diesel costs $0.

On the choice and number of microarrays for transcriptional regulatory network inference.

Background: Transcriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)-gene interactions at the genome-wide level. In correlation-based TRNI, network edges can in principle be evaluated using standard statistical tests. However, while such tests nominally assume independent microarray experiments, we expect dependency between the experiments in microarray compendia, due to both project-specific factors (e.

Reverse-engineering transcription control networks.

Microarray technologies, which enable the simultaneous measurement of all RNA transcripts in a cell, have spawned the development of algorithms for reverse-engineering transcription control networks. In this article, we classify the algorithms into two general strategies: physical modeling and influence modeling. We discuss the biological and computational principles underlying each strategy, and provide leading examples of each.

Quorum-sensing regulation of a copper toxicity system in Pseudomonas aeruginosa.

The LasR/LasI quorum-sensing system in Pseudomonas aeruginosa influences global gene expression and mediates pathogenesis. In this study, we show that the quorum-sensing system activates, via the transcriptional regulator PA4778, a copper resistance system composed of 11 genes. The quorum-sensing global regulator LasR was recently shown to directly activate transcription of PA4778, a cueR homolog and a MerR-type transcriptional regulator.

Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia.

Motivation: DNA microarrays are routinely applied to study diseased or drug-treated cell populations. A critical challenge is distinguishing the genes directly affected by these perturbations from the hundreds of genes that are indirectly affected. Here, we developed a sparse simultaneous equation model (SSEM) of mRNA expression data and applied Lasso regression to estimate the model parameters, thus constructing a network model of gene interaction effects.

Towards environmental systems biology of Shewanella.

Bacteria of the genus Shewanella are known for their versatile electron-accepting capacities, which allow them to couple the decomposition of organic matter to the reduction of the various terminal electron acceptors that they encounter in their stratified environments. Owing to their diverse metabolic capabilities, shewanellae are important for carbon cycling and have considerable potential for the remediation of contaminated environments and use in microbial fuel cells. Systems-level analysis of the model species Shewanella oneidensis MR-1 and other members of this genus has provided new insights into the signal-transduction proteins, regulators, and metabolic and respiratory subsystems that govern the remarkable versatility of the shewanellae.

Identification of diverse carbon utilization pathways in Shewanella oneidensis MR-1 via expression profiling.

To identify pathways of carbon utilization in the metal-reducing marine bacterium Shewanella oneidensis MR-1, we assayed the expression of cells grown with various carbon sources using a high-density oligonucleotide Affymetrix microarray. Our expression profiles reveal genes and regulatory mechanisms which govern the sensing, import, and utilization of the nucleoside inosine, the chitin monomer N-acetylglucosamine, and a casein-derived mixture of amino acids. Our analysis suggests a prominent role for the pentose-phosphate and Entner-Doudoroff pathways in energy metabolism, and regulatory coupling between carbon catabolism and electron acceptor pathways.

Predicting sigma28 promoters in eleven Shewanella genomes.

An iterative position-specific score matrix (PSSM)-based approach was used to predict sigma(28) promoters in 11 Shewanella genomes. The Shewanella Correlation Browser was used to distinguish true-positive predictions from false-positive predictions in Shewanella oneidensis MR-1 by generating a sigma(28)-regulated transcriptional network from transcriptional profiling data. This dual-pronged approach identified several genes that have sigma(28) promoters and that may be involved with motility or chemotaxis in Shewanella.

Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata.

Many Microbe Microarrays Database (M3D) is designed to facilitate the analysis and visualization of expression data in compendia compiled from multiple laboratories. M3D contains over a thousand Affymetrix microarrays for Escherichia coli, Saccharomyces cerevisiae and Shewanella oneidensis. The expression data is uniformly normalized to make the data generated by different laboratories and researchers more comparable.

Lightweight genome viewer: portable software for browsing genomics data in its chromosomal context.

Background: Lightweight genome viewer (lwgv) is a web-based tool for visualization of sequence annotations in their chromosomal context. It performs most of the functions of larger genome browsers, while relying on standard flat-file formats and bypassing the database needs of most visualization tools. Visualization as an aide to discovery requires display of novel data in conjunction with static annotations in their chromosomal context.

The mode-of-action by network identification (MNI) algorithm: a network biology approach for molecular target identification.

This protocol details the use of the mode-of-action by network identification (MNI) algorithm to identify the gene targets of a drug treatment based on gene-expression data. Investigators might also use the MNI algorithm to identify the gene mediators of a disease or the physiological state of cells and tissues. The MNI algorithm uses a training data set of hundreds of expression profiles to construct a statistical model of gene-regulatory networks in a cell or tissue.

Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.

Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E.

Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks.

A major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network.

Programmable cells: interfacing natural and engineered gene networks.

Novel cellular behaviors and characteristics can be obtained by coupling engineered gene networks to the cell's natural regulatory circuitry through appropriately designed input and output interfaces. Here, we demonstrate how an engineered genetic circuit can be used to construct cells that respond to biological signals in a predetermined and programmable fashion. We employ a modular design strategy to create Escherichia coli strains where a genetic toggle switch is interfaced with: (i) the SOS signaling pathway responding to DNA damage, and (ii) a transgenic quorum sensing signaling pathway from Vibrio fischeri.

Inferring genetic networks and identifying compound mode of action via expression profiling.

The complexity of cellular gene, protein, and metabolite networks can hinder attempts to elucidate their structure and function. To address this problem, we used systematic transcriptional perturbations to construct a first-order model of regulatory interactions in a nine-gene subnetwork of the SOS pathway in Escherichia coli. The model correctly identified the major regulatory genes and the transcriptional targets of mitomycin C activity in the subnetwork.