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The plant hormone auxin is fundamental for plant growth, and its spatial distribution in plant tissues is critical for plant morphogenesis. We consider a leading model of the polar auxin flux, and study in full detail the stability of the possible equilibrium configurations. We show that the critical states of the auxin transport process are composed of basic building blocks, which are isolated in a background of auxin depleted cells, and are not geometrically regular in general. The same model was considered recently through a continuous limit and a coupling to the von Karman equations, to model the interplay of biochemistry and mechanics during plant growth. Our conclusions might be of interest in this setting, since, for example, we establish the existence of Lyapunov functions for the auxin flux, proving in this way the convergence of pure transport processes toward the set of critical configurations., Comment: 27 pages

International Journal of Computer Vision, 140 (1), ISSN:0920-5691, ISSN:1573-1405

The enteroinvasive bacterium Shigella flexneri invades the intestinal epithelium of humans. During infection, several injected effector proteins promote bacterial internalization, and interfere with multiple host cell responses. To obtain a systems-level overview of host signaling during infection, we analyzed the global dynamics of protein phosphorylation by liquid chromatography-tandem MS and identified several hundred of proteins undergoing a phosphorylation change during the first hours of infection. Functional bioinformatic analysis revealed that they were mostly related to the cytoskeleton, transcription, signal transduction, and cell cycle. Fuzzy c-means clustering identified six temporal profiles of phosphorylation and a functional module composed of ATM-phosphorylated proteins related to genotoxic stress. Pathway enrichment analysis defined mTOR as the most overrepresented pathway. We showed that mTOR complex 1 and 2 were required for S6 kinase and AKT activation, respectively. Comparison with a published phosphoproteome of Salmonella typhimurium-infected cells revealed a large subset of coregulated phosphoproteins. Finally, we showed that S. flexneri effector OspF affected the phosphorylation of several hundred proteins, thereby demonstrating the wide-reaching impact of a single bacterial effector on the host signaling network.

Bioinformatics, 29 (20), ISSN:1367-4803, ISSN:1460-2059

Active contours constitute a computationally attractive framework for image segmentation. In this paper, we describe a fully parametric design that relies on B-spline bases and is specified by control points on the image. Our technique yields successful segmentation results even for challenging datasets where object contours are not well-defined. We can achieve this because our parametric approach uses few parameters and allows us to constrain the topology of the curve. We provide the details for an efficient implementation and discuss different design alternatives. This work comes with a companion software that gives the end user full control over the snakes. We demonstrate the features of our software which includes an interactive interface where the user can manipulate the position of control points by simple mouse actions. (C) 2013 Elsevier Masson SAS. All rights reserved

Hydrogel nanoparticles (HNP) are an emerging tool of biomedicine with unique materials characteristics, scope, and utility. These hydrated, soft colloidal carriers can penetrate through voids with dimensions narrower than the size of the particle, provide stabilization for fragile biological cargo and allow diffusion and exchange of solutes with external phase. However, techniques to assemble HNP are few; solitary examples exist of biocompatible polymers being formulated into HNP; and knowledge on the biomedical properties of HNP remains rather cursory. In this work, we investigate assembly of HNP based on a polymer with decades of prominence in the biomedical field, poly(vinyl alcohol), PVA. We develop a novel method for production of PVA HNP through nanoprecipitation-based assembly of polymer nanoparticles and subsequent physical hydrogelation of the polymer. Polymer nanoparticles and HNP were visualized using scanning electron microscopy and fluorescence imaging, and characterized using dynamic light scattering and zeta potential measurements. Interaction of PVA HNP with mammalian cells was investigated using flow cytometry, viability screening, and measurements of nitric oxide production by cultured macrophages. The latter analyses revealed that PVA administered as a polymer solution or in the form of HNP resulted in no measurable increase in production of the inflammation marker. Unexpectedly, PVA HNP exerted a pronounced inhibition of NO synthesis by stimulated macrophages, that is, had an anti-inflammatory activity. This effect was accomplished with a negligible change in the cell viability and was not observed when PVA was administered as a polymer solution. To the best of our knowledge, this is the first observation of inhibition of NO synthesis in macrophages by administered nanoparticles and specifically hydrogel nanoparticles. Taken together, our results present PVA HNP as promising colloidal hydrogel nanocarriers for biomedical applications, specifically drug delivery and assembly of intracellular biosensors.

Lipids have highly diverse functions that go beyond cellular membrane structure and energy storage. One of the great challenges in lipid research will be to understand how the enormous complexity of lipid homeostasis is maintained. Genetic approaches combined with mass spectrometry-based lipidomics will help to elucidate how cells create and maintain their nonrandom lipid distribution within tissues, cells, organelles and lipid bilayers. Lipid homeostasis is crucial for many cellular processes and we are currently only beginning to understand the specific functions of lipids and the local environment that they create.

Toxoplasma gondii belongs to the coccidian subgroup of the Apicomplexa phylum. The Coccidia are obligate intracellular pathogens that establish infection in their mammalian host via the enteric route. These parasites lack a mitochondrial pyruvate dehydrogenase complex but have preserved the degradation of branched-chain amino acids (BCAA) as a possible pathway to generate acetyl-CoA. Importantly, degradation of leucine, isoleucine and valine could lead to concomitant accumulation of propionyl-CoA, a toxic metabolite that inhibits cell growth. Like fungi and bacteria, the Coccidia possess the complete set of enzymes necessary to metabolize and detoxify propionate by oxidation to pyruvate via the 2-methylcitrate cycle (2-MCC). Phylogenetic analysis provides evidence that the 2-MCC was acquired via horizontal gene transfer. In T. gondii tachyzoites, this pathway is split between the cytosol and the mitochondrion. Although the rate-limiting enzyme 2-methylisocitrate lyase is dispensable for parasite survival, its substrates accumulate in parasites deficient in the enzyme and its absence confers increased sensitivity to propionic acid. BCAA is also dispensable in tachyzoites, leaving unresolved the source of mitochondrial acetyl-CoA.

Although changes in chromatin are integral to transcriptional reprogramming during cellular differentiation, it is currently unclear how chromatin modifications are targeted to specific loci. To systematically identify transcription factors (TFs) that can direct chromatin changes during cell fate decisions, we model the relationship between genome-wide dynamics of chromatin marks and the local occurrence of computationally predicted TF binding sites. By applying this computational approach to a time course of Polycomb-mediated H3K27me3 marks during neuronal differentiation of murine stem cells, we identify several motifs that likely regulate the dynamics of this chromatin mark. Among these, the sites bound by REST and by the SNAIL family of TFs are predicted to transiently recruit H3K27me3 in neuronal progenitors. We validate these predictions experimentally and show that absence of REST indeed causes loss of H3K27me3 at target promoters in trans, specifically at the neuronal progenitor state. Moreover, using targeted transgenic insertion, we show that promoter fragments containing REST or SNAIL binding sites are sufficient to recruit H3K27me3 in cis, while deletion of these sites results in loss of H3K27me3. These findings illustrate that the occurrence of TF binding sites can determine chromatin dynamics. Local determination of Polycomb activity by REST and SNAIL motifs exemplifies such TF based regulation of chromatin. Furthermore, our results show that key TFs can be identified ab initio through computational modeling of epigenome data sets using a modeling approach that we make readily accessible.

SummaryGene expression profiling has uncovered the transcription factor Sox4 with upregulated activity during TGF-β-induced epithelial-mesenchymal transition (EMT) in normal and cancerous breast epithelial cells. Sox4 is indispensable for EMT and cell survival in vitro and for primary tumor growth and metastasis in vivo. Among several EMT-relevant genes, Sox4 directly regulates the expression of Ezh2, encoding the Polycomb group histone methyltransferase that trimethylates histone 3 lysine 27 (H3K27me3) for gene repression. Ablation of Ezh2 expression prevents EMT, whereas forced expression of Ezh2 restores EMT in Sox4-deficient cells. Ezh2-mediated H3K27me3 marks associate with key EMT genes, representing an epigenetic EMT signature that predicts patient survival. Our results identify Sox4 as a master regulator of EMT by governing the expression of the epigenetic modifier Ezh2.

In the mammalian neocortex, segregated processing streams are thought to be important for forming sensory representations of the environment(1,2), but how local information in primary sensory cortex is transmitted to other distant cortical areas during behaviour is unclear. Here we show task-dependent activation of distinct, largely non-overlapping long-range projection neurons in the whisker region of primary somatosensory cortex (S1) in awake, behaving mice. Using two-photon calcium imaging, we monitored neuronal activity in anatomically identified S1 neurons projecting to secondary somatosensory (S2) or primary motor (M1) cortex in mice using their whiskers to perform a texture-discrimination task or a task that required them to detect the presence of an object at a certain location. Whisking-related cells were found among S2-projecting (S2P) but not M1-projecting (M1P) neurons. A higher fraction of S2P than M1P neurons showed touch-related responses during texture discrimination, whereas a higher fraction of M1P than S2P neurons showed touch-related responses during the detection task. In both tasks, S2P and M1P neurons could discriminate similarly between trials producing different behavioural decisions. However, in trials producing the same decision, S2P neurons performed better at discriminating texture, whereas M1P neurons were better at discriminating location. Sensory stimulus features alone were not sufficient to elicit these differences, suggesting that selective transmission of S1 information to S2 and M1 is driven by behaviour.

Quantification of mechanical properties of tissues, living cells, and cellular components is crucial for the modeling of plant developmental processes such as mechanotransduction. Pollen tubes are tip-growing cells that provide an ideal system to study the mechanical properties at the single cell level. In this article, a lab-on-a-chip (LOC) device is developed to quantitatively measure the biomechanical properties of lily (Lilium longiflorum) pollen tubes. A single pollen tube is fixed inside the microfluidic chip at a specific orientation and subjected to compression by a soft membrane. By comparing the deformation of the pollen tube at a given external load (compressibility) and the effect of turgor pressure on the tube diameter (stretch ratio) with finite element modeling, its mechanical properties are determined. The turgor pressure and wall stiffness of the pollen tubes are found to decrease considerably with increasing initial diameter of the pollen tubes. This observation supports the hypothesis that tip-growth is regulated by a delicate balance between turgor pressure and wall stiffness. The LOC device is modular and adaptable to a variety of cells that exhibit tip-growth, allowing for the straightforward measurement of mechanical properties.

Understanding the mechanisms of plant growth and morphogenesis is of importance, not only to the field of plant sciences but also in a broader agricultural and economical context, as plants provide important raw materials such as food, fiber, wood and fuel. Mathematical modeling and simulation can provide insights into intricate biomechanical growth mechanisms. Mechanical characterization at single cellular level is essential for providing quantitative model parameters. The dual-axis Cellular Force Microscope (CFM) provide many of these desired parameters by micro-indentation measurements. The dual-axis CFM integrates a MEMS force sensing technology with a piezoelectric nano-positioning system. The dual force feedback from the MEMS sensor allows the system to be used for arbitrary indentation angles and to accurately measure biological samples despite their curved surfaces. The accuracy of the dual-axis CFM is verified by measuring a force-standard with a certified stiffness of 15.15 N/m at indentation angles of 90° and 60°. The obtained stiffness measurements are 15.60 N/m and 14.63 N/m and differ from the calibrated value by 2.97% and 3.63%, respectively. Additionally, the dual-axis CFM offers topographic imaging which was used to visualize two hemispherical structures fabricated using two-photon photopolymerization with radii of 5 µm and 7.5 µm. The potential of the system to study plant cell mechanics at a single cell level is shown by indenting a Lilium longiflorum pollen tube along its axis at the tip. The topographical information combined with the recorded force feedback in axial and lateral directions show promising results for further mechanical characterization of plant cells using the dual-axis CFM.

Mapping gene regulatory networks is a significant challenge in systems biology, yet only a few methods are currently capable of systems-level identification of transcription factors (TFs) that bind a specific regulatory element. We developed a microfluidic method for integrated systems-level interaction mapping of TF-DNA interactions, generating and interrogating an array of 423 full-length Drosophila TFs. With integrated systems-level interaction mapping, it is now possible to rapidly and quantitatively map gene regulatory networks of higher eukaryotes.

During vertebrate embryogenesis, the rhythmic and sequential segmentation of the body axis is regulated by an oscillating genetic network termed the segmentation clock. We describe a new dynamic model for the core pace-making circuit of the zebrafish segmentation clock based on a systematic biochemical investigation of the network's topology and precise measurements of somitogenesis dynamics in novel genetic mutants. We show that the core pace-making circuit consists of two distinct negative feedback loops, one with Her1 homodimers and the other with Her7:Hes6 heterodimers, operating in parallel. To explain the observed single and double mutant phenotypes of her1, her7, and hes6 mutant embryos in our dynamic model, we postulate that the availability and effective stability of the dimers with DNA binding activity is controlled in a "dimer cloud" that contains all possible dimeric combinations between the three factors. This feature of our model predicts that Hes6 protein levels should oscillate despite constant hes6 mRNA production, which we confirm experimentally using novel Hes6 antibodies. The control of the circuit's dynamics by a population of dimers with and without DNA binding activity is a new principle for the segmentation clock and may be relevant to other biological clocks and transcriptional regulatory networks. © 2012 Schröter et al. Fil: Schroter, Christian. University of Cambridge; Estados Unidos Fil: Ares, Saúl. Max Planck Institute For The Physics Of Complex Systems; Alemania Fil: Morelli, Luis Guillermo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina Fil: Isakova, Alina. Ecole Polytechnique Federale de Lausanne; Francia Fil: Hens, Korneel. Ecole Polytechnique Federale de Lausanne; Francia Fil: Soroldoni, Daniele. Max Planck Institute Of Molecular Cell Biology And Genetics; Alemania Fil: Gajewski, Martin. Universitat zu Köln; Alemania Fil: Jülicher, Frank. Max Planck Institute For The Physics Of Complex Systems; Alemania Fil: Maerkl, Sebastian J.. Ecole Polytechnique Federale de Lausanne; Francia Fil: Deplancke, Bart. Ecole Polytechnique Federale de Lausanne; Francia Fil: Oates, Andrew C.. Max Planck Institute Of Molecular Cell Biology And Genetics; Alemania

Understanding synaptic connectivity and plasticity within brain circuits and their relationship to learning and behavior is a fundamental quest in neuroscience. Visualizing the fine details of synapses using optical microscopy remains however a major technical challenge. Super resolution microscopy opens the possibility to reveal molecular features of synapses beyond the diffraction limit. With direct stochastic optical reconstruction microscopy, dSTORM, we image synaptic proteins in the brain tissue of the fruit fly, Drosophila melanogaster. Super resolution imaging of brain tissue harbors difficulties due to light scattering and the density of signals. In order to reduce out of focus signal, we take advantage of the genetic tools available in the Drosophila and have fluorescently tagged synaptic proteins expressed in only a small number of neurons. These neurons form synapses within the calyx of the mushroom body, a distinct brain region involved in associative memory formation. Our results show that super resolution microscopy, in combination with genetically labeled synaptic proteins, is a powerful tool to investigate synapses in a quantitative fashion providing an entry point for studies on synaptic plasticity during learning and memory formation.

The genomic loci occupied by RNA polymerase (RNAP) III have been characterized in human culture cells by genome-wide chromatin immunoprecipitations, followed by deep sequencing (ChIP-seq). These studies have shown that only ∼40% of the annotated 622 human tRNA genes and pseudogenes are occupied by RNAP-III, and that these genes are often in open chromatin regions rich in active RNAP-II transcription units. We have used ChIP-seq to characterize RNAP-III-occupied loci in a differentiated tissue, the mouse liver. Our studies define the mouse liver RNAP-III-occupied loci including a conserved mammalian interspersed repeat (MIR) as a potential regulator of an RNAP-III subunit-encoding gene. They reveal that synteny relationships can be established between a number of human and mouse RNAP-III genes, and that the expression levels of these genes are significantly linked. They establish that variations within the A and B promoter boxes, as well as the strength of the terminator sequence, can strongly affect RNAP-III occupancy of tRNA genes. They reveal correlations with various genomic features that explain the observed variation of 81% of tRNA scores. In mouse liver, loci represented in the NCBI37/mm9 genome assembly that are clearly occupied by RNAP-III comprise 50 Rn5s (5S RNA) genes, 14 known non-tRNA RNAP-III genes, nine Rn4.5s (4.5S RNA) genes, and 29 SINEs. Moreover, out of the 433 annotated tRNA genes, half are occupied by RNAP-III. Transfer RNA gene expression levels reflect both an underlying genomic organization conserved in dividing human culture cells and resting mouse liver cells, and the particular promoter and terminator strengths of individual genes.

We present a microfluidic cell-culture chip that enables trapping, cultivation and release of selected individual cells. The chip is fabricated by a simple hybrid glass-SU-8-PDMS approach, which produces a completely transparent microfluidic system amenable to optical inspection. Single cells are trapped in a microfluidic channel using mild suction at defined cell immobilization orifices, where they are cultivated under controlled environmental conditions. Cells of interest can be individually and independently released for further downstream analysis by applying a negative dielectrophoretic force via the respective electrodes located at each immobilization site. The combination of hydrodynamic cell-trapping and dielectrophoretic methods for cell releasing enables highly versatile single-cell manipulation in an array-based format. Computational fluid dynamics simulations were performed to estimate the properties of the system during cell trapping and releasing. Polystyrene beads and yeast cells have been used to investigate and characterize the different functions and to demonstrate biological compatibility and viability of the platform for single-cell applications in research areas such as systems biology.

Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image‐based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single‐cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome‐wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell‐based screens at this depth reveals widespread RNAi‐induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell‐to‐cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large‐scale RNAi screens are increasingly performed to reach a systems‐level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single‐cell microenvironment., Molecular Systems Biology, 8 (1), ISSN:1744-4292

Microscopy can provide invaluable information about biological processes at the single cell level. It remains a challenge, however, to extract quantitative information from these types of datasets. We have developed an image analysis platform named YeastQuant to simplify data extraction by offering an integrated method to turn time-lapse movies into single cell measurements. This platform is based on a database with a graphical user interface where the users can describe their experiments. The database is connected to the engineering software Matlab, which allows extracting the desired information by automatically segmenting and quantifying the microscopy images. We implemented three different segmentation methods that recognize individual cells under different conditions, and integrated image analysis protocols that allow measuring and analyzing distinct cellular readouts. To illustrate the power and versatility of YeastQuant, we investigated dynamic signal transduction processes in yeast. First, we quantified the expression of fluorescent reporters induced by osmotic stress to study noise in gene expression. Second, we analyzed the dynamic relocation of endogenous proteins from the cytoplasm to the cell nucleus, which provides a fast measure of pathway activity. These examples demonstrate that YeastQuant provides a versatile and expandable database and an experimental framework that improves image analysis and quantification of diverse microscopy-based readouts. Such dynamic single cell measurements are highly needed to establish mathematical models of signal transduction pathways.

Humans are noted for their capacity to over-ride self-interest in favor of normatively valued goals. We examined the neural circuitry that is causally involved in normative, fairness-related decisions by generating a temporarily diminished capacity for costly normative behavior, a 'deviant' case, through non-invasive brain stimulation (repetitive transcranial magnetic stimulation) and compared normal subjects' functional magnetic resonance imaging signals with those of the deviant subjects. When fairness and economic self-interest were in conflict, normal subjects (who make costly normative decisions at a much higher frequency) displayed significantly higher activity in, and connectivity between, the right dorsolateral prefrontal cortex (DLPFC) and the posterior ventromedial prefrontal cortex (pVMPFC). In contrast, when there was no conflict between fairness and economic self-interest, both types of subjects displayed identical neural patterns and behaved identically. These findings suggest that a parsimonious prefrontal network, the activation of right DLPFC and pVMPFC, and the connectivity between them, facilitates subjects' willingness to incur the cost of normative decisions.

Mitogen-activated protein kinase (MAPK) cascades are conserved signaling modules that control many cellular processes by integrating intra- and extracellular cues. The p38/Hog1 MAPK is transiently activated in response to osmotic stress, leading to rapid translocation into the nucleus and induction of a specific transcriptional program. When investigating the dynamic interplay between Hog1 activation and Hog1-driven gene expression, we found that Hog1 activation increases linearly with stimulus, whereas the transcriptional output is bimodal. Modeling predictions, corroborated by single-cell experiments, established that a slow stochastic transition from a repressed to an activated transcriptional state in conjunction with transient Hog1 activation generates this behavior. Together, these findings provide a molecular mechanism by which a cell can impose a transcriptional threshold in response to a linear signaling behavior.

The simulation of the creation and evolution of biological forms requires the development of computational methods that are capable of resolving their hierarchical, spatial and temporal complexity. Computations based on interacting particles, provide a unique computational tool for discrete and continuous descriptions of morphogenesis of systems ranging from the molecular to the organismal level. The capabilities of particle methods hinge on the simplicity of their formulation which enables the formulation of a unifying computational framework encompassing deterministic and stochastic models. In this paper, we discuss recent advances in particle methods for the simulation of biological systems at the mesoscopic and the macroscale level. We present results from applications of particle methods including reaction–diffusion on deforming surfaces, deterministic and stochastic descriptions of tumor growth and angiogenesis and discuss successes and challenges of this approach.

With a targeted proteomic approach, we could quantify 90% of the enzymes involved in carbon and amino-acid metabolism in yeast, including complete isoenzyme families, throughout a set of different metabolic states. The data, interpreted through flux balance modeling, indicate that S. cerevisiae expresses enzymes, not necessarily used in a particular metabolic condition. For many isoenzymes our data suggest functional diversification, which might explain their parallel presence in the S. cerevisiae genome., The metabolic network in the yeast Saccharomyces cerevisiae has been very well characterized in terms of components and topology. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. In this study, we exploited quantitative proteomic assays based on selected reaction monitoring (SRM) mass spectrometry to comprehensively analyze the set of enzymes involved in carbon and amino-acid metabolism in yeast (Figure 1), throughout a set of different metabolic states. To elucidate how this metabolic network of proteins adapts to environmental challenges, we chose five nutritional conditions resulting in maximal difference in magnitude and direction of metabolic fluxes. We could reproducibly detect and quantify across the different conditions, 90% of the targeted metabolic proteome, including complete families of isoenzymes, sharing up to 99.5% sequence identity and multi-subunit enzyme complexes. This yielded an information-rich proteomic data set that represents a nutritionally perturbed biological system with high coverage of its components. Interpreted through flux balance modeling, the data indicate that S. cerevisiae expresses—at least at a basal level—more proteins than are actually necessary for sustaining a given metabolic condition. One potential explanation for the presence of non-necessary proteins is that these enzymes could realize immediate basal metabolic fluxes upon a change to new environmental conditions. Next, we asked whether our data set could contribute to unravel the function of isoenzymes in the metabolic set. Previously proposed roles for isoenzymes include redundancy to buffer against mutations, a means to gene dosage or facilitation of evolutionary innovation and functional diversification. To address the role of isoenzymes in central metabolism, we used hierarchical clustering to analyze the abundance patterns of the metabolic proteins and their relationship to different functional classes and metabolic pathways. Interestingly, while subunits of the same protein complex preferably cluster in proximate branches, members of the same isoenzyme family often clustered in distant branches (Figure 5). The data therefore suggested functional diversification within most isoenzyme families and allowed to propose different functions of divergent isoenzymes. We expect that the comprehensive data set presented in this study will be an ideal blueprint for further developing models of yeast metabolism and for follow-up studies on the function of target metabolic proteins., Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95–99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes.

DNA technologies such as cloning, DNA microarrays, and next generation sequencing have transformed the life sciences. Protein technologies on the other hand have not seen such explosive progress. This is mainly due to the inherent difficulty of working with proteins because of their manifold physical characteristics as opposed to the well behaved and well understood DNA polymer. Recent technological advancements have increased the throughput of protein biochemistry to levels where it is becoming of interest to systems biology. Here I review methods for high-throughput in situ synthesis and characterization of proteins and their integration with microfluidic devices. In the near future, the use of gene synthesis, microfluidic based protein synthesis and characterization will give rise to a resurgence of protein biochemistry in the current world of high-throughput genomics.

SummaryThe target of rapamycin (TOR) is a highly conserved protein kinase and a central controller of growth. Mammalian TOR complex 2 (mTORC2) regulates AGC kinase family members and is implicated in various disorders, including cancer and diabetes. Here, we investigated the upstream regulation of mTORC2. A genetic screen in yeast and subsequent studies in mammalian cells revealed that ribosomes, but not protein synthesis, are required for mTORC2 signaling. Active mTORC2 was physically associated with the ribosome, and insulin-stimulated PI3K signaling promoted mTORC2-ribosome binding, suggesting that ribosomes activate mTORC2 directly. Findings with melanoma and colon cancer cells suggest that mTORC2-ribosome association is important in oncogenic PI3K signaling. Thus, TORC2-ribosome interaction is a likely conserved mechanism of TORC2 activation that is physiologically relevant in both normal and cancer cells. As ribosome content determines growth capacity of a cell, this mechanism of TORC2 regulation ensures that TORC2 is active only in growing cells.

In this article, we introduce a method using nanoscale ion-pair reversed-phase high-performance liquid chromatography (nano-IP-RP-HPLC) hyphenated to nanoelectrospray ionization high-resolution mass spectrometry (nano-ESI-HRMS) to separate and identify metabolites in cell extracts. Separation of metabolites was performed on a 100 μm i.d. C18 column with tributylamine (TBA) as the ion-pairing reagent and methanol as the eluent. Basic pH (9.4) of the mobile phase was critical to achieve sufficient retention and sharp metabolite elution at a low concentration of TBA (1.7 mM). Limits of detection were determined for 54 standards with an LTQ-Orbitrap mass spectrometer to be in the upper attomole to low femtomole range for key metabolites such as nucleotides, phosphorylated sugars, organic acids, and coenzyme A thioesters in solvent as well as in a complex matrix. To further evaluate the method, metabolome analysis was performed injecting different amounts of biomass of the methylotroph model organism Methylobacterium extorquens AM1. A (12)C/(13)C labeling strategy was implemented to improve metabolite identification. Analysis of three biological replicates performed with 1.5 ng of cell dry weight biomass equivalents resulted in the identification of 20 ± 4 metabolites, and analysis of 150 ng allowed identifying 157 ± 5 metabolites from a large spectrum of metabolite classes.

Thermodynamic analysis of metabolic networks has recently generated increasing interest for its ability to add constraints on metabolic network operation, and to combine metabolic fluxes and metabolite measurements in a mechanistic manner. Concepts for the calculation of the change in Gibbs energy of biochemical reactions have long been established. However, a concept for incorporation of cross-membrane transport in these calculations is still missing, although the theory for calculating thermodynamic properties of transport processes is long known. Here, we have developed two equivalent equations to calculate the change in Gibbs energy of combined transport and reaction processes based on two different ways of treating biochemical thermodynamics. We illustrate the need for these equations by showing that in some cases there is a significant difference between the proposed correct calculation and using an approximative method. With the developed equations, thermodynamic analysis of metabolic networks spanning over multiple physical compartments can now be correctly described.

We performed microsecond long coarse-grained molecular dynamics simulations to elucidate the lateral structure and domain dynamics of a phospha- tidylethanolamine (PE)/phosphatidylglycerol (PG) mixed bilayer (7/3), mimicking the inner membrane of gram-negative bacteria. Specifically, we address the effect of surface bound antimicrobial peptides (AMPs) on the lateral organization of the membrane.Wefindthat, intheabsenceofthe peptides,theminor PGfractiononly forms small clusters, but that these clusters grow in size upon binding of the cationic AMPs. The presence of AMPs systematically affects the dynamics and induces long-range order in the structure of PG domains, stabilizing the separation between the two lipid fractions. Our results help in understanding the initial stages of destabilization of cytoplasmic bacterial membranes below the critical peptide concentration necessary for disruption, and provide a possible explanation for the multimodal character of AMP activity. SECTION Surfactants, Membranes

Specific binding of transcription factors (TFs) determines in a large part the connectivity of gene regulatory networks as well as the quantitative level of gene expression. A multiplicity of both experimental and computational methods is currently used to discover and characterize the underlying TF–DNA interactions. Experimental methods can be further subdivided into in vitro- and in vivo-based approaches, each accenting different aspects of TF-binding events. In this review we summarize the flexibility and performance of a selection of both types of experimental methods. In conclusion, we argue that a serial combination of methods with different throughput and data type constitutes an optimal experimental strategy.

Several stochastic models have tried to capture the architecture of food webs. This approach is interesting, but it is limited by the fact that different assumptions can yield similar results. To overcome this limitation, we develop a purely statistical approach. Body size in terms of an optimal ratio between prey and predator is used as explanatory variable. In 12 observed food webs, this model predicts, on average, 20% of interactions. To analyze the unexplained part, we introduce a latent term: each species is described by two latent traits, foraging and vulnerability, that represent nonmeasured characteristics of species once the optimal body size has been accounted for. The model now correctly predicts an average of 73% of links. The key features of our approach are that latent traits quantify the structure that is left unexplained by the explanatory variable and that this quantification allows a test of whether independent biological information, such as microhabitat use, camouflage, or phylogeny, explains this structure. We illustrate this method with phylogeny and find that it is linked to one or both latent traits in nine of 12 food webs. Our approach opens the door to the formulation of more complex models that can be applied to any kind of biological network.

Contains fulltext : 87926.pdf (Publisher’s version ) (Open Access) Theories of the positive symptoms of schizophrenia hypothesize a role for aberrant reinforcement signaling driven by dysregulated dopamine transmission. Recently, we provided evidence of aberrant reward learning in symptomatic, but not asymptomatic patients with schizophrenia, using a novel paradigm, the Salience Attribution Test (SAT). The SAT is a probabilistic reward learning game that employs cues that vary across task-relevant and task-irrelevant dimensions; it provides behavioral indices of adaptive and aberrant reward learning. As an initial step prior to future clinical studies, here we used functional magnetic resonance imaging to examine the neural basis of adaptive and aberrant reward learning during the SAT in healthy volunteers. As expected, cues associated with high relative to low reward probabilities elicited robust hemodynamic responses in a network of structures previously implicated in motivational salience; the midbrain, in the vicinity of the ventral tegmental area, and regions targeted by its dopaminergic projections, i.e. medial dorsal thalamus, ventral striatum and prefrontal cortex (PFC). Responses in the medial dorsal thalamus and polar PFC were strongly correlated with the degree of adaptive reward learning across participants. Finally, and most importantly, differential dorsolateral PFC and middle temporal gyrus (MTG) responses to cues with identical reward probabilities were very strongly correlated with the degree of aberrant reward learning. Participants who showed greater aberrant learning exhibited greater dorsolateral PFC responses, and reduced MTG responses, to cues erroneously inferred to be less strongly associated with reward. The results are discussed in terms of their implications for different theories of associative learning. 8 p.

Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. We assess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression data remains an unsolved problem, and they indicate potential ways of network reconstruction improvements.

Benzodiazepines are widely used in clinics and for recreational purposes, but will lead to addiction in vulnerable individuals. Addictive drugs increase the levels of dopamine and also trigger long-lasting synaptic adaptations in the mesolimbic reward system that ultimately may induce the pathological behaviour. The neural basis for the addictive nature of benzodiazepines, however, remains elusive. Here we show that benzodiazepines increase firing of dopamine neurons of the ventral tegmental area through the positive modulation of GABA(A) (gamma-aminobutyric acid type A) receptors in nearby interneurons. Such disinhibition, which relies on alpha1-containing GABA(A) receptors expressed in these cells, triggers drug-evoked synaptic plasticity in excitatory afferents onto dopamine neurons and underlies drug reinforcement. Taken together, our data provide evidence that benzodiazepines share defining pharmacological features of addictive drugs through cell-type-specific expression of alpha1-containing GABA(A) receptors in the ventral tegmental area. The data also indicate that subunit-selective benzodiazepines sparing alpha1 may be devoid of addiction liability.

Inflammation contributes to both insulin resistance and pancreatic beta cell failure in human type 2 diabetes. Toll-like receptors (TLRs) are highly conserved pattern recognition receptors that coordinate the innate inflammatory response to numerous substances, including NEFAs. Here we investigated a potential contribution of TLR2 to the metabolic dysregulation induced by high-fat diet (HFD) feeding in mice. Male and female littermate Tlr2 +/+ and Tlr2 −/− mice were analysed with respect to glucose tolerance, insulin sensitivity, insulin secretion and energy metabolism on chow and HFD. Adipose, liver, muscle and islet pathology and inflammation were examined using molecular approaches. Macrophages and dendritic immune cells, in addition to pancreatic islets were investigated in vitro with respect to NEFA-induced cytokine production. While not showing any differences in glucose homeostasis on chow diet, both male and female Tlr2 −/− mice were protected from the adverse effects of HFD compared with Tlr2 +/+ littermate controls. Female Tlr2 −/− mice showed pronounced improvements in glucose tolerance, insulin sensitivity, and insulin secretion following 20 weeks of HFD feeding. These effects were associated with an increased capacity of Tlr2 −/− mice to preferentially burn fat, combined with reduced tissue inflammation. Bone-marrow-derived dendritic cells and pancreatic islets from Tlr2 −/− mice did not increase IL-1β expression in response to a NEFA mixture, whereas Tlr2 +/+ control tissues did. These data suggest that TLR2 is a molecular link between increased dietary lipid intake and the regulation of glucose homeostasis, via regulation of energy substrate utilisation and tissue inflammation.

In yeast TOR and PKA pathways both control cell growth but how TORC1 and PKA signaling are linked is unknown. Here we show that TORC1 inhibition prevents the phosphorylation of some but not all PKA targets. We further demonstrate that TORC1 controls PKA by inhibiting the phosphorylation of the PKA regulatory subunit BCY1 by the MAP kinase MPK1., Regulation of cell growth requires extensive coordination of several processes including transcription, ribosome biogenesis, translation, nutrient metabolism, and autophagy. In yeast, the protein kinases Target of Rapamycin (TOR) and protein kinase A (PKA) regulate these processes and are thereby the main activators of cell growth in response to nutrients. How TOR, PKA, and their corresponding signaling pathways are coordinated to control the same cellular processes is not understood. Quantitative analysis of the rapamycin-sensitive phosphoproteome combined with targeted analysis of PKA substrates suggests that TOR complex 1 (TORC1) activates PKA but only toward a subset of substrates. Furthermore, we show that TORC1 signaling impinges on BCY1, the negative regulatory subunit of PKA. Inhibition of TORC1 with rapamycin leads to BCY1 phosphorylation on several sites including T129. Phosphorylation of BCY1 T129 results in BCY1 activation and inhibition of PKA. TORC1 inhibits BCY1 T129 phosphorylation by phosphorylating and activating the S6K homolog SCH9 that in turn inhibits the MAP kinase MPK1. MPK1 phosphorylates BCY1 T129 directly. Thus, TORC1 activates PKA toward some substrates by preventing MPK1-mediated activation of BCY1.

Both biosociological and psychological models, as well as animal research, suggest that testosterone has a key role in social interactions(1-7). Evidence from animal studies in rodents shows that testosterone causes aggressive behaviour towards conspecifics(7). Folk wisdom generalizes and adapts these findings to humans, suggesting that testosterone induces antisocial, egoistic, or even aggressive human behaviours. However, many researchers have questioned this folk hypothesis(1-6), arguing that testosterone is primarily involved in status-related behaviours in challenging social interactions, but causal evidence that discriminates between these views is sparse. Here we show that the sublingual administration of a single dose of testosterone in women causes a substantial increase in fair bargaining behaviour, thereby reducing bargaining conflicts and increasing the efficiency of social interactions. However, subjects who believed that they received testosterone-regardless of whether they actually received it or not-behaved much more unfairly than those who believed that they were treated with placebo. Thus, the folk hypothesis seems to generate a strong negative association between subjects' beliefs and the fairness of their offers, even though testosterone administration actually causes a substantial increase in the frequency of fair bargaining offers in our experiment.

In most plants, a large fraction of photo-assimilated carbon is stored in the chloroplasts during the day as starch and remobilized during the subsequent night to support metabolism. Mutations blocking either starch synthesis or starch breakdown in Arabidopsis thaliana reduce plant growth. Maltose is the major product of starch breakdown exported from the chloroplast at night. The maltose excess 1 mutant (mex1), which lacks the chloroplast envelope maltose transporter, accumulates high levels of maltose and starch in chloroplasts and develops a distinctive but previously unexplained chlorotic phenotype as leaves mature. The introduction of additional mutations that prevent starch synthesis, or that block maltose production from starch, also prevent chlorosis of mex1. In contrast, introduction of mutations in disproportionating enzyme (DPE1) results in the accumulation of maltotriose in addition to maltose, and greatly increases chlorosis. These data suggest a link between maltose accumulation and chloroplast homeostasis. Microscopic analyses show that the mesophyll cells in chlorotic mex1 leaves have fewer than half the number of chloroplasts than wild-type cells. Transmission electron microscopy reveals autophagy-like chloroplast degradation in both mex1 and the dpe1/mex1 double mutant. Microarray analyses reveal substantial reprogramming of metabolic and cellular processes, suggesting that organellar protein turnover is increased in mex1, though leaf senescence and senescence-related chlorophyll catabolism are not induced. We propose that the accumulation of maltose and malto-oligosaccharides causes chloroplast dysfunction, which may by signaled via a form of retrograde signaling and trigger chloroplast degradation.

Functional integration in the brain rests on anatomical connectivity (the presence of axonal connections) and effective connectivity (the causal influences mediated by these connections). The deployment of anatomical connections provides important constraints on effective connectivity, but does not fully determine it, because synaptic connections can be expressed functionally in a dynamic and context-dependent fashion. Although it is generally assumed that anatomical connectivity data is important to guide the construction of neurobiologically realistic models of effective connectivity; the degree to which these models actually profit from anatomical constraints has not yet been formally investigated. Here, we use diffusion weighted imaging and probabilistic tractography to specify anatomically informed priors for dynamic causal models (DCMs) of fMRI data. We constructed 64 alternative DCMs, which embodied different mappings between the probability of an anatomical connection and the prior variance of the corresponding of effective connectivity, and fitted them to empirical fMRI data from 12 healthy subjects. Using Bayesian model selection, we show that the best model is one in which anatomical probability increases the prior variance of effective connectivity parameters in a nonlinear and monotonic (sigmoidal) fashion. This means that the higher the likelihood that a given connection exists anatomically, the larger one should set the prior variance of the corresponding coupling parameter; hence making it easier for the parameter to deviate from zero and represent a strong effective connection. To our knowledge, this study provides the first formal evidence that probabilistic knowledge of anatomical connectivity can improve models of functional integration.

Live imaging of subcellular structures is indispensible to advance our understanding of cellular processes. The blurred digital images acquired in light microscopy are, however, complex to analyze, and identification and reconstruction of subcellular structures from such images remains a major challenge. We present a novel, model-based image analysis algorithm to reconstruct outlines of subcellular structures using a sub-pixel representation. The algorithm explicitly accounts for the optical properties of the microscope. We validate the reconstruction performance on synthetic data and apply the new method to fluorescence microscopy images of endosomes identified by the GTPase EGFP-Rab5. The benefits of the new algorithm are outlined by comparison to standard techniques. We demonstrate that the new algorithm leads to better discrimination between different endosomal virus entry pathways and to more robust, accurate, and self-consistent quantification of endosome shape features. This allows establishing a set of features that quantify endosome morphology and robustly capture the dynamics of endosome fusion.

Recent advances in cancer research have identified critical angiogenic signaling pathways and the influence of the extracellular matrix on endothelial cell migration. These findings provide us with insight into the process of angiogenesis that can facilitate the development of effective computational models of sprouting angiogenesis. In this work, we present the first three-dimensional model of sprouting angiogenesis that considers explicitly the effect of the extracellular matrix and of the soluble as well as matrix-bound growth factors on capillary growth. The computational model relies on a hybrid particle-mesh representation of the blood vessels and it introduces an implicit representation of the vasculature that can accommodate detailed descriptions of nutrient transport. Extensive parametric studies reveal the role of the extracellular matrix structure and the distribution of the different vascular endothelial growth factors isoforms on the dynamics and the morphology of the generated vascular networks.

Pollen tubes are used as a model in the study of plant morphogenesis, cellular differentiation, cell wall biochemistry, biomechanics, and intra- and intercellular signaling. For a “systems-understanding” of the bio-chemo-mechanics of tip-polarized growth in pollen tubes, the need for a versatile, experimental assay platform for quantitative data collection and analysis is critical. We introduce a Lab-on-a-Chip (LoC) concept for high-throughput pollen germination and pollen tube guidance for parallelized optical and mechanical measurements. The LoC localizes a large number of growing pollen tubes on a single plane of focus with unidirectional tip-growth, enabling high-resolution quantitative microscopy. This species-independent LoC platform can be integrated with micro-/nano-indentation systems, such as the cellular force microscope (CFM) or the atomic force microscope (AFM), allowing for rapid measurements of cell wall stiffness of growing tubes. As a demonstrative example, we show the growth and directional guidance of hundreds of lily (Lilium longiflorum) and Arabidopsis (Arabidopsis thaliana) pollen tubes on a single LoC microscopy slide. Combining the LoC with the CFM, we characterized the cell wall stiffness of lily pollen tubes. Using the stiffness statistics and finite-element-method (FEM)-based approaches, we computed an effective range of the linear elastic moduli of the cell wall spanning the variability space of physiological parameters including internal turgor, cell wall thickness, and tube diameter. We propose the LoC device as a versatile and high-throughput phenomics platform for plant reproductive and development biology using the pollen tube as a model., PLoS ONE, 11 (12), ISSN:1932-6203

Computational Mechanics, 56 (5), ISSN:0178-7675, ISSN:1432-0924

Uneven illumination affects every image acquired by a microscope. It is often overlooked, but it can introduce considerable bias to image measurements. The most reliable correction methods require special reference images, and retrospective alternatives do not fully model the correction process. Our approach overcomes these issues for most optical microscopy applications without the need for reference images.

Background New experimental methods must be developed to study interaction networks in systems biology. To reduce biological noise, individual subjects, such as single cells, should be analyzed using high throughput approaches. The measurement of several correlative physical properties would further improve data consistency. Accordingly, a considerable quantity of data must be acquired, correlated, catalogued and stored in a database for subsequent analysis. Results We have developed openBEB (open Biological Experiment Browser), a software framework for data acquisition, coordination, annotation and synchronization with database solutions such as openBIS. OpenBEB consists of two main parts: A core program and a plug-in manager. Whereas the data-type independent core of openBEB maintains a local container of raw-data and metadata and provides annotation and data management tools, all data-specific tasks are performed by plug-ins. The open architecture of openBEB enables the fast integration of plug-ins, e.g., for data acquisition or visualization. A macro-interpreter allows the automation and coordination of the different modules. An update and deployment mechanism keeps the core program, the plug-ins and the metadata definition files in sync with a central repository. Conclusions The versatility, the simple deployment and update mechanism, and the scalability in terms of module integration offered by openBEB make this software interesting for a large scientific community. OpenBEB targets three types of researcher, ideally working closely together: (i) Engineers and scientists developing new methods and instruments, e.g., for systems-biology, (ii) scientists performing biological experiments, (iii) theoreticians and mathematicians analyzing data. The design of openBEB enables the rapid development of plug-ins, which will inherently benefit from the “house keeping” abilities of the core program. We report the use of openBEB to combine live cell microscopy, microfluidic control and visual proteomics. In this example, measurements from diverse complementary techniques are combined and correlated.

Bacteria are thought to cope with fluctuating environmental solute concentrations primarily by adjusting the osmolality of their cytoplasm. To obtain insights into the underlying metabolic adaptations, we analyzed the global metabolic response of Escherichia coli to sustained hyperosmotic stress using nontargeted mass spectrometry. We observed that 52% of 1,071 detected metabolites, including known osmoprotectants, changed abundance with increasing salt challenge. Unexpectedly, unsupervised data analysis showed a substantial increase of most intermediates in the ubiquinone-8 (Q8) biosynthesis pathway and a 110-fold accumulation of Q8 itself, as confirmed by quantitative lipidomics. We then demonstrated that Q8 is necessary for acute and sustained osmotic-stress tolerance using Q8 mutants and tolerance rescue through feeding nonrespiratory Q8 analogs. Finally, in vitro experiments with artificial liposomes showed that mechanical membrane stabilization is a principal mechanism of Q8-mediated osmoprotection. Thus, we find that besides regulating intracellular osmolality, E. coli enhances its cytoplasmic membrane stability to withstand osmotic stress.

Electron crystallography is used to study membrane proteins in the form of planar, two-dimensional (2D) crystals, or other crystalline arrays such as tubular crystals. This method has been used to determine the atomic resolution structures of bacteriorhodopsin, tubulin, aquaporins, and several other membrane proteins. In addition, a large number of membrane protein structures were studied at a slightly lower resolution, whereby at least secondary structure motifs could be identified.In order to conserve the structural details of delicate crystalline arrays, cryo-electron microscopy (cryo-EM) allows imaging and/or electron diffraction of membrane proteins in their close-to-native state within a lipid bilayer membrane.To achieve ultimate high-resolution structural information of 2D crystals, meticulous sample preparation for electron crystallography is of outmost importance. Beam-induced specimen drift and lack of specimen flatness can severely affect the attainable resolution of images for tilted samples. Sample preparations that sandwich the 2D crystals between symmetrical carbon films reduce the beam-induced specimen drift, and the flatness of the preparations can be optimized by the choice of the grid material and the preparation protocol.Data collection in the cryo-electron microscope using either the imaging or the electron diffraction mode has to be performed applying low-dose procedures. Spot-scanning further reduces the effects of beam-induced drift. Data collection using automated acquisition schemes, along with improved and user-friendlier data processing software, is increasingly being used and is likely to bring the technique to a wider user base.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to determine, within entire genomes, the occupancy sites of any protein of interest, including, for example, transcription factors, RNA polymerases, or histones with or without various modifications. In addition to allowing the determination of occupancy sites within one cell type and under one condition, this method allows, in principle, the establishment and comparison of occupancy maps in various cell types, tissues, and conditions. Such comparisons require, however, that samples be normalized. Widely used normalization methods that include a quantile normalization step perform well when factor occupancy varies at a subset of sites, but may miss uniform genome-wide increases or decreases in site occupancy. We describe a spike adjustment procedure (SAP) that, unlike commonly used normalization methods intervening at the analysis stage, entails an experimental step prior to immunoprecipitation. A constant, low amount from a single batch of chromatin of a foreign genome is added to the experimental chromatin. This “spike” chromatin then serves as an internal control to which the experimental signals can be adjusted. We show that the method improves similarity between replicates and reveals biological differences including global and largely uniform changes.

Splicing reactions generally combine high speed with accuracy. However, some of the pre-mRNAs escape the nucleus with a retained intron. Intron retention can control gene expression and increase proteome diversity. We calculated the escape rate for the yeast PTC7 intron and pre-mRNA. This prediction was facilitated by the observation that splicing is a linear process and by deriving simple algebraic expressions from a model of co- and post-transcriptional splicing and RNA surveillance that determines the rate of the nonsense-mediated decay (NMD) of the pre-mRNAs with the retained intron. The escape rate was consistent with the observed threshold of splicing rate below which the mature mRNA level declined. When an mRNA contains multiple introns, the outcome of splicing becomes more difficult to predict since not only the escape rate of the pre-mRNA has to be considered, but also the possibility that the splicing of each intron is influenced by the others. We showed that the two adjacent introns in the SUS1 mRNA are spliced cooperatively, but this does not counteract the escape of the partially spliced mRNA. These findings will help to infer promoter activity and to predict the behavior of and to control splicing regulatory networks.