Molecular mechanisms of system responses to novel stimuli are predictable from public data

MtrA modulates Mycobacterium tuberculosis cell division in host microenvironments to mediate intrinsic resistance and drug tolerance

The success of Mycobacterium tuberculosis (Mtb) is largely attributed to its ability to physiologically adapt and withstand diverse localized stresses within host microenvironments. Here, we present a data-driven model (EGRIN 2.0) that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. Analysis of EGRIN 2.0 shows how modulation of the MtrAB two-component signaling system tunes Mtb growth in response to related host microenvironmental cues. Disruption of MtrAB by tunable CRISPR interference confirms that the signaling system regulates multiple peptidoglycan hydrolases, among other targets, that are important for cell division. Further, MtrA decreases the effectiveness of antibiotics by mechanisms of both intrinsic resistance and drug tolerance. Together, the model-enabled dissection of complex MtrA regulation highlights its importance as a drug target and illustrates how EGRIN 2.0 facilitates discovery and mechanistic characterization of Mtb adaptation to specific host microenvironments within the host.

A systems-level gene regulatory network model for Plasmodium falciparum

Many of the gene regulatory processes of Plasmodium falciparum, the deadliest malaria parasite, remain poorly understood. To develop a comprehensive guide for exploring this organism's gene regulatory network, we generated a systems-level model of P. falciparum gene regulation using a well-validated, machine-learning approach for predicting interactions between transcription regulators and their targets. The resulting network accurately predicts expression levels of transcriptionally coherent gene regulatory programs in independent transcriptomic data sets from parasites collected by different research groups in diverse laboratory and field settings. Thus, our results indicate that our gene regulatory model has predictive power and utility as a hypothesis-generating tool for illuminating clinically relevant gene regulatory mechanisms within P. falciparum. Using the set of regulatory programs we identified, we also investigated correlates of artemisinin resistance based on gene expression coherence. We report that resistance is associated with incoherent expression across many regulatory programs, including those controlling genes associated with erythrocyte-host engagement. These results suggest that parasite populations with reduced artemisinin sensitivity are more transcriptionally heterogenous. This pattern is consistent with a model where the parasite utilizes bet-hedging strategies to diversify the population, rendering a subpopulation more able to navigate drug treatment.

Peroxisome prognostications: Exploring the birth, life, and death of an organelle

Peroxisomes play a central role in human health and have biochemical properties that promote their use in many biotechnology settings. With a primary role in lipid metabolism, peroxisomes share a niche with lipid droplets within the endomembrane-secretory system. Notably, factors in the ER required for the biogenesis of peroxisomes also impact the formation of lipid droplets. The dynamic interface between peroxisomes and lipid droplets, and also between these organelles and the ER and mitochondria, controls their metabolic flux and their dynamics. Here, we review our understanding of peroxisome biogenesis to propose and reframe models for understanding how peroxisomes are formed in cells. To more fully understand the roles of peroxisomes and to take advantage of their many properties that may prove useful in novel therapeutics or biotechnology applications, we recast mechanisms controlling peroxisome biogenesis in a framework that integrates inference from these models with experimental data.

Selective Translation of Low Abundance and Upregulated Transcripts in Halobacterium salinarum

Our findings demonstrate conclusively that low abundance and upregulated transcripts are preferentially translated, potentially by environment-specific translation systems with distinct ribosomal protein composition. We show that a complex interplay of transcriptional and posttranscriptional regulation underlies the conditional and modular regulatory programs that generate ribosomes of distinct protein composition. The modular regulation of ribosomal proteins with other transcription, translation, and metabolic genes is generalizable to bacterial and eukaryotic microbes. These findings are relevant to how microorganisms adapt to unfavorable environments when they transition from active growth to quiescence by generating proteins from upregulated transcripts that are in considerably lower abundance relative to transcripts associated with the previous physiological state. Selective translation of transcripts by distinct ribosomes could form the basis for adaptive evolution to new environments through a modular regulation of the translational systems.

When organisms encounter an unfavorable environment, they transition to a physiologically distinct, quiescent state wherein abundant transcripts from the previous active growth state continue to persist, albeit their active transcription is downregulated. In order to generate proteins for the new quiescent physiological state, we hypothesized that the translation machinery must selectively translate upregulated transcripts in an intracellular milieu crowded with considerably higher abundance transcripts from the previous active growth state. Here, we have analyzed genome-wide changes in the transcriptome (RNA sequencing [RNA-seq]), changes in translational regulation and efficiency by ribosome profiling across all transcripts (ribosome profiling [Ribo-seq]), and protein level changes in assembled ribosomal proteins (sequential window acquisition of all theoretical mass spectra [SWATH-MS]) to investigate the interplay of transcriptional and translational regulation in Halobacterium salinarum as it transitions from active growth to quiescence. We have discovered that interplay of regulatory processes at different levels of information processing generates condition-specific ribosomal complexes to translate preferentially pools of low abundance and upregulated transcripts. Through analysis of the gene regulatory network architecture of H. salinarum, Escherichia coli, and Saccharomyces cerevisiae, we demonstrate that this conditional, modular organization of regulatory programs governing translational systems is a generalized feature across all domains of life.

IMPORTANCE Our findings demonstrate conclusively that low abundance and upregulated transcripts are preferentially translated, potentially by environment-specific translation systems with distinct ribosomal protein composition. We show that a complex interplay of transcriptional and posttranscriptional regulation underlies the conditional and modular regulatory programs that generate ribosomes of distinct protein composition. The modular regulation of ribosomal proteins with other transcription, translation, and metabolic genes is generalizable to bacterial and eukaryotic microbes. These findings are relevant to how microorganisms adapt to unfavorable environments when they transition from active growth to quiescence by generating proteins from upregulated transcripts that are in considerably lower abundance relative to transcripts associated with the previous physiological state. Selective translation of transcripts by distinct ribosomes could form the basis for adaptive evolution to new environments through a modular regulation of the translational systems.

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR’s final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes.

OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling

The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.

Characterization of Peroxisomal Regulation Networks

Peroxisome proliferation involves signal recognition and computation by molecular networks that direct molecular events of gene expression, metabolism, membrane biogenesis, organelle proliferation, protein import, and organelle inheritance. Peroxisome biogenesis in yeast has served as a model system for exploring the regulatory networks controlling this process. Yeast is an outstanding model system to develop tools and approaches to study molecular networks and cellular responses and because the mechanisms of peroxisome biogenesis and key aspects of the transcriptional regulatory networks are remarkably conserved from yeast to humans. In this chapter, we focus on the complex regulatory networks that respond to environmental cues leading to peroxisome assembly and the molecular events of organelle assembly. Ultimately, understanding the mechanisms of the entire peroxisome biogenesis program holds promise for predictive modeling approaches and for guiding rational intervention strategies that could treat human conditions associated with peroxisome function.

The Promise of Systems Biology Approaches for Revealing Host Pathogen Interactions in Malaria

Despite global eradication efforts over the past century, malaria remains a devastating public health burden, causing almost half a million deaths annually (WHO, 2016). A detailed understanding of the mechanisms that control malaria infection has been hindered by technical challenges of studying a complex parasite life cycle in multiple hosts. While many interventions targeting the parasite have been implemented, the complex biology of Plasmodium poses a major challenge, and must be addressed to enable eradication. New approaches for elucidating key host-parasite interactions, and predicting how the parasite will respond in a variety of biological settings, could dramatically enhance the efficacy and longevity of intervention strategies. The field of systems biology has developed methodologies and principles that are well poised to meet these challenges. In this review, we focus our attention on the Liver Stage of the Plasmodium lifecycle and issue a “call to arms” for using systems biology approaches to forge a new era in malaria research. These approaches will reveal insights into the complex interplay between host and pathogen, and could ultimately lead to novel intervention strategies that contribute to malaria eradication.

MAMMOTh: A new database for curated mathematical models of biomolecular systems

MOTIVATION

Living systems have a complex hierarchical organization that can be viewed as a set of dynamically interacting subsystems. Thus, to simulate the internal nature and dynamics of the entire biological system, we should use the iterative way for a model reconstruction, which is a consistent composition and combination of its elementary subsystems. In accordance with this bottom-up approach, we have developed the MAthematical Models of bioMOlecular sysTems (MAMMOTh) tool that consists of the database containing manually curated MAMMOTh fitted to the experimental data and a software tool that provides their further integration.

RESULTS

The MAMMOTh database entries are organized as building blocks in a way that the model parts can be used in different combinations to describe systems with higher organizational level (metabolic pathways and/or transcription regulatory networks). The tool supports export of a single model or their combinations in SBML or Mathematica standards. The database currently contains 110 mathematical sub-models for Escherichia coli elementary subsystems (enzymatic reactions and gene expression regulatory processes) that can be combined in at least 5100 complex/sophisticated models concerning more complex biological processes as de novo nucleotide biosynthesis, aerobic/anaerobic respiration and nitrate/nitrite utilization in E. coli. All models are functionally interconnected and sufficiently complement public model resources.

AVAILABILITY

http://mammoth.biomodelsgroup.ru.

Increased heme synthesis in yeast induces a metabolic switch from fermentation to respiration even under conditions of glucose repression

Regulation of mitochondrial biogenesis and respiration is a complex process that involves several signaling pathways and transcription factors as well as communication between the nuclear and mitochondrial genomes. Under aerobic conditions, the budding yeast Saccharomyces cerevisiae metabolizes glucose predominantly by glycolysis and fermentation. We have recently shown that altered chromatin structure in yeast induces respiration by a mechanism that requires transport and metabolism of pyruvate in mitochondria. However, how pyruvate controls the transcriptional responses underlying the metabolic switch from fermentation to respiration is unknown. Here, we report that this pyruvate effect involves heme. We found that heme induces transcription of HAP4, the transcriptional activation subunit of the Hap2/3/4/5p complex, required for growth on nonfermentable carbon sources, in a Hap1p- and Hap2/3/4/5p-dependent manner. Increasing cellular heme levels by inactivating ROX1, which encodes a repressor of many hypoxic genes, or by overexpressing HEM3 or HEM12 induced respiration and elevated ATP levels. Increased heme synthesis, even under conditions of glucose repression, activated Hap1p and the Hap2/3/4/5p complex and induced transcription of HAP4 and genes required for the tricarboxylic acid (TCA) cycle, electron transport chain, and oxidative phosphorylation, leading to a switch from fermentation to respiration. Conversely, inhibiting metabolic flux into the TCA cycle reduced cellular heme levels and HAP4 transcription. Together, our results indicate that the glucose-mediated repression of respiration in budding yeast is at least partly due to the low cellular heme level.

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions

Plants rely on transcriptional dynamics to respond to multiple climatic fluctuations and contexts in nature. We analyzed the genome-wide gene expression patterns of rice (Oryza sativa) growing in rainfed and irrigated fields during two distinct tropical seasons and determined simple linear models that relate transcriptomic variation to climatic fluctuations. These models combine multiple environmental parameters to account for patterns of expression in the field of co-expressed gene clusters. We examined the similarities of our environmental models between tropical and temperate field conditions, using previously published data. We found that field type and macroclimate had broad impacts on transcriptional responses to environmental fluctuations, especially for genes involved in photosynthesis and development. Nevertheless, variation in solar radiation and temperature at the timescale of hours had reproducible effects across environmental contexts. These results provide a basis for broad-based predictive modeling of plant gene expression in the field.

DOI:http://dx.doi.org/10.7554/eLife.08411.001

Plants need to be able to sense and respond to changes in temperature, light levels and other aspects of their environment. One way in which plants can rapidly respond to these changes is to modify how genes involved in growth and other processes are expressed. Therefore, understanding how this happens may help us to improve the ability of crops to grow when exposed to drought or other extreme environmental conditions.

Most previous studies into the effect of the environment on plant gene expression have been carried out under controlled conditions in a laboratory. These findings cannot reflect the full range of gene expression patterns that occur in the natural environment, where multiple factors (e.g. sunlight, water, nutrients) may vary at the same time. Therefore, it is important to also analyze the effect of fluctuations in multiple environmental factors in more complex field experiments.

Plessis et al. developed mathematical models to analyze the gene expression patterns of rice plants grown in the tropical environment of the Philippines using two different farming practices. One field of rice was flooded and constantly supplied with fresh water (referred to as the irrigated field), while the other field was dry and only received water from rainfall (the rainfed field). The experiments show that temperature and levels of sunlight (including UV radiation) have a strong impact on gene expression in the rice plants. Short-term variations in temperature and sunlight levels also have the most consistent effect across the different fields and seasons tested. However, for many genes, the plants grown in the irrigated field responded to the changes in environmental conditions in a different way to the plants grown in the rainfed field.

Further analysis identified groups of genes whose expression combined responses to several environmental factors at the same time. For example, certain genes that responded to increases in sunlight in the absence of drought responded to both sunlight levels and the shortage of water when a drought occurred. The next step is to test more types of environments and climates to be able to predict gene expression responses under future climatic conditions.

DOI:http://dx.doi.org/10.7554/eLife.08411.002

cMonkey2: Automated, systematic, integrated detection of co-regulated gene modules for any organism

The cMonkey integrated biclustering algorithm identifies conditionally co-regulated modules of genes (biclusters). cMonkey integrates various orthogonal pieces of information which support evidence of gene co-regulation, and optimizes biclusters to be supported simultaneously by one or more of these prior constraints. The algorithm served as the cornerstone for constructing the first global, predictive Environmental Gene Regulatory Influence Network (EGRIN) model for a free-living cell, and has now been applied to many more organisms. However, due to its computational inefficiencies, long run-time and complexity of various input data types, cMonkey was not readily usable by the wider community. To address these primary concerns, we have significantly updated the cMonkey algorithm and refactored its implementation, improving its usability and extendibility. These improvements provide a fully functioning and user-friendly platform for building co-regulated gene modules and the tools necessary for their exploration and interpretation. We show, via three separate analyses of data for E. coli, M. tuberculosis and H. sapiens, that the updated algorithm and inclusion of novel scoring functions for new data types (e.g. ChIP-seq and transcription factor over-expression [TFOE]) improve discovery of biologically informative co-regulated modules. The complete cMonkey2 software package, including source code, is available at https://github.com/baliga-lab/cmonkey2.

Thermal variation, thermal extremes and the physiological performance of individuals

In this review we consider how small-scale temporal and spatial variation in body temperature, and biochemical/physiological variation among individuals, affect the prediction of organisms' performance in nature. For ‘normal’ body temperatures – benign temperatures near the species' mean – thermal biology traditionally uses performance curves to describe how physiological capabilities vary with temperature. However, these curves, which are typically measured under static laboratory conditions, can yield incomplete or inaccurate predictions of how organisms respond to natural patterns of temperature variation. For example, scale transition theory predicts that, in a variable environment, peak average performance is lower and occurs at a lower mean temperature than the peak of statically measured performance. We also demonstrate that temporal variation in performance is minimized near this new ‘optimal’ temperature. These factors add complexity to predictions of the consequences of climate change. We then move beyond the performance curve approach to consider the effects of rare, extreme temperatures. A statistical procedure (the environmental bootstrap) allows for long-term simulations that capture the temporal pattern of extremes (a Poisson interval distribution), which is characterized by clusters of events interspersed with long intervals of benign conditions. The bootstrap can be combined with biophysical models to incorporate temporal, spatial and physiological variation into evolutionary models of thermal tolerance. We conclude with several challenges that must be overcome to more fully develop our understanding of thermal performance in the context of a changing climate by explicitly considering different forms of small-scale variation. These challenges highlight the need to empirically and rigorously test existing theories.

Considerations for the use of transcriptomics in identifying the ‘genes that matter’ for environmental adaptation

Transcriptomics has emerged as a powerful approach for exploring physiological responses to the environment. However, like any other experimental approach, transcriptomics has its limitations. Transcriptomics has been criticized as an inappropriate method to identify genes with large impacts on adaptive responses to the environment because: (1) genes with large impacts on fitness are rare; (2) a large change in gene expression does not necessarily equate to a large effect on fitness; and (3) protein activity is most relevant to fitness, and mRNA abundance is an unreliable indicator of protein activity. In this review, these criticisms are re-evaluated in the context of recent systems-level experiments that provide new insight into the relationship between gene expression and fitness during environmental stress. In general, these criticisms remain valid today, and indicate that exclusively using transcriptomics to screen for genes that underlie environmental adaptation will overlook constitutively expressed regulatory genes that play major roles in setting tolerance limits. Standard practices in transcriptomic data analysis pipelines may also be limiting insight by prioritizing highly differentially expressed and conserved genes over those genes that undergo moderate fold-changes and cannot be annotated. While these data certainly do not undermine the continued and widespread use of transcriptomics within environmental physiology, they do highlight the types of research questions for which transcriptomics is best suited and the need for more gene functional analyses. Such information is pertinent at a time when transcriptomics has become increasingly tractable and many researchers may be contemplating integrating transcriptomics into their research programs.

Signaling dynamics and peroxisomes

Peroxisomes are remarkably responsive organelles. Their composition, abundance and even their mechanism of biogenesis are influenced strongly by cell type and the environment. This plasticity underlies peroxisomal functions in metabolism and the detoxification of dangerous reactive oxygen species. However, peroxisomes are integrated into the cellular system as a whole such that they communicate intimately with other organelles, control signaling dynamics as in the case of innate immune responses to infectious disease, and contribute to processes as fundamental as longevity. The increasing evidence for peroxisomes having roles in various cellular and organismal functions, combined with their malleability, suggests complex mechanisms operate to control cellular dynamics and the specificity of cellular responses and functions extending well beyond the peroxisome itself. A deeper understanding of the functions of peroxisomes and the mechanisms that control their plasticity could offer opportunities for exploiting changes in peroxisome abundance to control cellular function.

Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions

Background

Biclustering is a popular method for identifying under which experimental conditions biological signatures are co-expressed. However, the general biclustering problem is NP-hard, offering room to focus algorithms on specific biological tasks. We hypothesize that conditional co-regulation of genes is a key factor in determining cell phenotype and that accurately segregating conditions in biclusters will improve such predictions. Thus, we developed a bicluster sampled coherence metric (BSCM) for determining which conditions and signals should be included in a bicluster.

Results

Our BSCM calculates condition and cluster size specific p-values, and we incorporated these into the popular integrated biclustering algorithm cMonkey. We demonstrate that incorporation of our new algorithm significantly improves bicluster co-regulation scores (p-value = 0.009) and GO annotation scores (p-value = 0.004). Additionally, we used a bicluster based signal to predict whether a given experimental condition will result in yeast peroxisome induction. Using the new algorithm, the classifier accuracy improves from 41.9% to 76.1% correct.

Conclusions

We demonstrate that the proposed BSCM helps determine which signals ought to be co-clustered, resulting in more accurately assigned bicluster membership. Furthermore, we show that BSCM can be extended to more accurately detect under which experimental conditions the genes are co-clustered. Features derived from this more accurate analysis of conditional regulation results in a dramatic improvement in the ability to predict a cellular phenotype in yeast. The latest cMonkey is available for download at https://github.com/baliga-lab/cmonkey2. The experimental data and source code featured in this paper is available http://AitchisonLab.com/BSCM. BSCM has been incorporated in the official cMonkey release.

Transcriptional program for nitrogen starvation-induced lipid accumulation in Chlamydomonas reinhardtii

BACKGROUND

Algae accumulate lipids to endure different kinds of environmental stresses including macronutrient starvation. Although this response has been extensively studied, an in depth understanding of the transcriptional regulatory network (TRN) that controls the transition into lipid accumulation remains elusive. In this study, we used a systems biology approach to elucidate the transcriptional program that coordinates the nitrogen starvation-induced metabolic readjustments that drive lipid accumulation in Chlamydomonas reinhardtii.

RESULTS

We demonstrate that nitrogen starvation triggered differential regulation of 2147 transcripts, which were co-regulated in 215 distinct modules and temporally ordered as 31 transcriptional waves. An early-stage response was triggered within 12 min that initiated growth arrest through activation of key signaling pathways, while simultaneously preparing the intracellular environment for later stages by modulating transport processes and ubiquitin-mediated protein degradation. Subsequently, central metabolism and carbon fixation were remodeled to trigger the accumulation of triacylglycerols. Further analysis revealed that these waves of genome-wide transcriptional events were coordinated by a regulatory program orchestrated by at least 17 transcriptional regulators, many of which had not been previously implicated in this process. We demonstrate that the TRN coordinates transcriptional downregulation of 57 metabolic enzymes across a period of nearly 4 h to drive an increase in lipid content per unit biomass. Notably, this TRN appears to also drive lipid accumulation during sulfur starvation, while phosphorus starvation induces a different regulatory program. The TRN model described here is available as a community-wide web-resource at http://networks.systemsbiology.net/chlamy-portal.

CONCLUSIONS

In this work, we have uncovered a comprehensive mechanistic model of the TRN controlling the transition from N starvation to lipid accumulation. The program coordinates sequentially ordered transcriptional waves that simultaneously arrest growth and lead to lipid accumulation. This study has generated predictive tools that will aid in devising strategies for the rational manipulation of regulatory and metabolic networks for better biofuel and biomass production.

Systems cell biology

Systems cell biology melds high-throughput experimentation with quantitative analysis and modeling to understand many critical processes that contribute to cellular organization and dynamics. Recently, there have been several advances in technology and in the application of modeling approaches that enable the exploration of the dynamic properties of cells. Merging technology and computation offers an opportunity to objectively address unsolved cellular mechanisms, and has revealed emergent properties and helped to gain a more comprehensive and fundamental understanding of cell biology.

Peroxisomes take shape

Peroxisomes carry out various oxidative reactions that are tightly regulated to adapt to the changing needs of the cell and varying external environments. Accordingly, they are remarkably fluid and can change dramatically in abundance, size, shape and content in response to numerous cues. These dynamics are controlled by multiple aspects of peroxisome biogenesis that are coordinately regulated with each other and with other cellular processes. Ongoing studies are deciphering the diverse molecular mechanisms that underlie biogenesis and how they cooperate to dynamically control peroxisome utility. These important challenges should lead to an understanding of peroxisome dynamics that can be capitalized upon for bioengineering and the development of therapies to improve human health.

Network portal: a database for storage, analysis and visualization of biological networks

The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal.