scispace - formally typeset
Search or ask a question

Showing papers in "Molecular Systems Biology in 2007"


Journal ArticleDOI
TL;DR: A protein‐network‐based approach is applied that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases, which provide novel hypotheses for pathways involved in tumor progression.
Abstract: Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.

1,495 citations


Journal ArticleDOI
TL;DR: An updated genome‐scale reconstruction of the metabolic network in Escherichia coli K‐12 MG1655 with increased scope and computational capability is presented, expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.
Abstract: An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.

1,445 citations


Journal ArticleDOI
TL;DR: In‐depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases, and via the preliminary analysis, many novel protein interactions and pathway associations are reported.
Abstract: Mapping protein-protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein-protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24,540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein-protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

996 citations


Journal ArticleDOI
TL;DR: It is shown that reverse‐engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements.
Abstract: Inferring, or ‘reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

897 citations


Journal ArticleDOI
TL;DR: The value of understanding fundamental and structural theories is that they provide deeper insights into the governing principles that complex evolvable systems including biological systems that define the possible design space of life.
Abstract: Mol Syst Biol. 3: 137 Robustness is one of the fundamental characteristics of biological systems. Numerous reports have been published on how robustness is involved in various biological processes and on mechanisms that give rise to robustness in living systems (Savageau, 1985a, 1985b, 1998; Barkai and Leibler, 1997; Alon et al , 1999; von Dassow et al , 2000; Bhalla and Iyengar, 2001; Csete and Doyle, 2002, 2004; Kitano et al , 2004, 2004a, 2004b; Stelling et al , 2004; Kitano and Oda, 2006; Kitano, 2007a). With increasing interest in systems biology, properties at the system level such as robustness have attracted serious scientific research. Nevertheless, a mathematical foundation that provides a unified perspective on robustness is yet to be established. For systems biology to mature into a solid scientific discipline, there must be a solid theoretical and methodological foundation. Often, systems biology is equated with computer simulation of cells and organs. Although computer simulation is a powerful technique for clarifying the complex dynamics of biological systems, it is also a useful tool for exploring the foundation of biological systems. While investigation on the dynamic properties of specific aspects of organisms is scientifically significant and can be widely applied, it is a study on specific instances of design within a design space that is shaped by fundamental principles, structural, environmental, and evolutionary constraints. The scientific goal of systems biology is not merely to create precision models of cells and organs, but also to discover fundamental and structural principles behind biological systems that define the possible design space of life (Figure 1). The value of understanding fundamental and structural theories is that they provide deeper insights into the governing principles that complex evolvable systems including biological …

783 citations


Journal ArticleDOI
TL;DR: This work systematically evaluates the capacity of 11 objective functions combined with eight adjustable constraints to predict 13C‐determined in vivo fluxes in Escherichia coli under six environmental conditions and identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints.
Abstract: To which extent can optimality principles describe the operation of metabolic networks? By explicitly considering experimental errors and in silico alternate optima in flux balance analysis, we systematically evaluate the capacity of 11 objective functions combined with eight adjustable constraints to predict 13C-determined in vivo fluxes in Escherichia coli under six environmental conditions. While no single objective describes the flux states under all conditions, we identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints. Unlimited growth on glucose in oxygen or nitrate respiring batch cultures is best described by nonlinear maximization of the ATP yield per flux unit. Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states.

742 citations


Journal ArticleDOI
TL;DR: The purpose of this perspective is to provide a logical basis for a new approach to classifying human disease that uses conventional reductionism and incorporates the non‐reductionist approach of systems biomedicine.
Abstract: Contemporary classification of human disease derives from observational correlation between pathological analysis and clinical syndromes. Characterizing disease in this way established a nosology that has served clinicians well to the current time, and depends on observational skills and simple laboratory tools to define the syndromic phenotype. Yet, this time-honored diagnostic strategy has significant shortcomings that reflect both a lack of sensitivity in identifying preclinical disease, and a lack of specificity in defining disease unequivocally. In this paper, we focus on the latter limitation, viewing it as a reflection both of the different clinical presentations of many diseases (variable phenotypic expression), and of the excessive reliance on Cartesian reductionism in establishing diagnoses. The purpose of this perspective is to provide a logical basis for a new approach to classifying human disease that uses conventional reductionism and incorporates the non-reductionist approach of systems biomedicine.

555 citations


Journal ArticleDOI
TL;DR: This work demonstrated that reconstructed metabolic networks and stoichiometric models can serve not only to predict metabolic fluxes and growth phenotypes of single organisms, but also to capture growth parameters and community composition of simple bacterial communities.
Abstract: The rate of production of methane in many environments depends upon mutualistic interactions between sulfate-reducing bacteria and methanogens. To enhance our understanding of these relationships, we took advantage of the fully sequenced genomes of Desulfovibrio vulgaris and Methanococcus maripaludis to produce and analyze the first multispecies stoichiometric metabolic model. Model results were compared to data on growth of the co-culture on lactate in the absence of sulfate. The model accurately predicted several ecologically relevant characteristics, including the flux of metabolites and the ratio of D. vulgaris to M. maripaludis cells during growth. In addition, the model and our data suggested that it was possible to eliminate formate as an interspecies electron shuttle, but hydrogen transfer was essential for syntrophic growth. Our work demonstrated that reconstructed metabolic networks and stoichiometric models can serve not only to predict metabolic fluxes and growth phenotypes of single organisms, but also to capture growth parameters and community composition of simple bacterial communities.

477 citations


Journal ArticleDOI
TL;DR: Data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level, impacting directly on the host's ability to metabolize lipids.
Abstract: Symbiotic gut microorganisms (microbiome) interact closely with the mammalian host's metabolism and are important determinants of human health. Here, we decipher the complex metabolic effects of microbial manipulation, by comparing germfree mice colonized by a human baby flora (HBF) or a normal flora to conventional mice. We perform parallel microbiological profiling, metabolic profiling by 1H nuclear magnetic resonance of liver, plasma, urine and ileal flushes, and targeted profiling of bile acids by ultra performance liquid chromatography–mass spectrometry and short-chain fatty acids in cecum by GC-FID. Top-down multivariate analysis of metabolic profiles reveals a significant association of specific metabotypes with the resident microbiome. We derive a transgenomic graph model showing that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize lipids: HBF mice present higher ileal concentrations of tauro-conjugated bile acids, reduced plasma levels of lipoproteins but higher hepatic triglyceride content associated with depletion of glutathione. These data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level.

463 citations


Journal ArticleDOI
Kwang Ho Lee1, Jin Hwan Park, Tae Yong Kim, Hyun Uk Kim, Sang Yup Lee 
TL;DR: The genetically defined L‐threonine overproducing Escherichia coli strain by systems metabolic engineering may be broadly employed for developing genetically defined organisms for the efficient production of various bioproducts.
Abstract: Amino-acid producers have traditionally been developed by repeated random mutagenesis owing to the difficulty in rationally engineering the complex and highly regulated metabolic network. Here, we report the development of the genetically defined L-threonine overproducing Escherichia coli strain by systems metabolic engineering. Feedback inhibitions of aspartokinase I and III (encoded by thrA and lysC, respectively) and transcriptional attenuation regulations (located in thrL) were removed. Pathways for Thr degradation were removed by deleting tdh and mutating ilvA. The metA and lysA genes were deleted to make more precursors available for Thr biosynthesis. Further target genes to be engineered were identified by transcriptome profiling combined with in silico flux response analysis, and their expression levels were manipulated accordingly. The final engineered E. coli strain was able to produce Thr with a high yield of 0.393 g per gram of glucose, and 82.4 g/l Thr by fed-batch culture. The systems metabolic engineering strategy reported here may be broadly employed for developing genetically defined organisms for the efficient production of various bioproducts.

456 citations


Journal ArticleDOI
TL;DR: By analysis of the functional connectivity of the metabolites in the network, the bow‐tie structure, which was found previously by structure analysis, is reconfirmed and the distribution of the disease related genes in thenetwork suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts.
Abstract: A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts.

Journal ArticleDOI
TL;DR: It is shown that superoxide‐mediated oxidation of iron–sulfur clusters promotes a breakdown of iron regulatory dynamics and drives the generation of highly destructive hydroxyl radicals via the Fenton reaction, and that blockage of hydroxy radical formation increases the survival of gyrase‐poisoned cells.
Abstract: Modulation of bacterial chromosomal supercoiling is a function of DNA gyrase-catalyzed strand breakage and rejoining. This reaction is exploited by both antibiotic and proteic gyrase inhibitors, which trap the gyrase molecule at the DNA cleavage stage. Owing to this interaction, doublestranded DNA breaks are introduced and replication machinery is arrested at blocked replication forks. This immediately results in bacteriostasis and ultimately induces cell death. Here we demonstrate, through a series of phenotypic and gene expression analyses, that superoxide and hydroxyl radical oxidative species are generated following gyrase poisoning and play an important role in cell killing by gyrase inhibitors. We show that superoxide-mediated oxidation of iron–sulfur clusters promotes a breakdown of iron regulatory dynamics; in turn, iron misregulation drives the generation of highly destructive hydroxyl radicals via the Fenton reaction. Importantly, our data reveal that blockage of hydroxyl radical formation increases the survival of gyrase-poisoned cells. Together, this series of biochemical reactions appears to compose a maladaptive response, that serves to amplify the primary effect of gyrase inhibition by oxidatively damaging DNA, proteins and lipids. Molecular Systems Biology 13 March 2007; doi:10.1038/msb4100135 Subject Categories: cellular metabolism; microbiology & pathogenesis

Journal ArticleDOI
TL;DR: A synthetic AND gate in the bacterium Escherichia coli is constructed that integrates information from two promoters as inputs and activates a promoter output only when both input promoters are transcriptionally active.
Abstract: Microorganisms use genetic circuits to integrate environmental information. We have constructed a synthetic AND gate in the bacterium Escherichia coli that integrates information from two promoters as inputs and activates a promoter output only when both input promoters are transcriptionally active. The integration occurs via an interaction between an mRNA and tRNA. The first promoter controls the transcription of a T7 RNA polymerase gene with two internal amber stop codons blocking translation. The second promoter controls the amber suppressor tRNA supD. When both components are transcribed, T7 RNA polymerase is synthesized and this in turn activates a T7 promoter. Because inputs and outputs are promoters, the design is modular; that is, it can be reconnected to integrate different input signals and the output can be used to drive different cellular responses. We demonstrate this modularity by wiring the gate to integrate natural promoters (responding to Mg2+ and AI-1) and using it to implement a phenotypic output (invasion of mammalian cells). A mathematical model of the transfer function is derived and parameterized using experimental data.

Journal ArticleDOI
TL;DR: A combinatorial library of random promoter architectures is constructed and heuristic rules for programming gene expression with combinatorially promoters are identified, including regulatory range, logic type, and symmetry.
Abstract: Promoters control the expression of genes in response to one or more transcription factors (TFs). The architecture of a promoter is the arrangement and type of binding sites within it. To understand natural genetic circuits and to design promoters for synthetic biology, it is essential to understand the relationship between promoter function and architecture. We constructed a combinatorial library of random promoter architectures. We characterized 288 promoters in Escherichia coli, each containing up to three inputs from four different TFs. The library design allowed for multiple −10 and −35 boxes, and we observed varied promoter strength over five decades. To further analyze the functional repertoire, we defined a representation of promoter function in terms of regulatory range, logic type, and symmetry. Using these results, we identified heuristic rules for programming gene expression with combinatorial promoters.

Journal ArticleDOI
TL;DR: It is shown quantitatively that regulation by sRNAs is advantageous when fast responses to external signals are needed, consistent with experimental data about its involvement in stress responses, and thatregulation by sRNA may provide fine‐tuning of gene expression.
Abstract: The importance of post-transcriptional regulation by small non-coding RNAs has recently been recognized in both pro- and eukaryotes. Small RNAs (sRNAs) regulate gene expression post-transcriptionally by base pairing with the mRNA. Here we use dynamical simulations to characterize this regulation mode in comparison to transcriptional regulation mediated by protein–DNA interaction and to post-translational regulation achieved by protein–protein interaction. We show quantitatively that regulation by sRNA is advantageous when fast responses to external signals are needed, consistent with experimental data about its involvement in stress responses. Our analysis indicates that the half-life of the sRNA–mRNA complex and the ratio of their production rates determine the steady-state level of the target protein, suggesting that regulation by sRNA may provide fine-tuning of gene expression. We also describe the network of regulation by sRNA in Escherichia coli, and integrate it with the transcription regulation network, uncovering mixed regulatory circuits, such as mixed feed-forward loops. The integration of sRNAs in feed-forward loops provides tight repression, guaranteed by the combination of transcriptional and post-transcriptional regulations.

Journal ArticleDOI
TL;DR: A comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer‐associated genetically and epigenetically altered genes finds that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer‐ associated methylated genes are enrichment in negative signaling regulatory loop.
Abstract: We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancer-signaling architectural and functional organization. From the network, we extracted an oncogene-signaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few 'oncogene-signaling-dependent blocks' in which frequently used oncogene-signaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancer-associated genes and biomarkers.

Journal ArticleDOI
TL;DR: This paper presents a new method, steady‐state regulatory flux balance analysis (SR‐FBA), for predicting gene expression and metabolic fluxes in a large‐scale integrated metabolic–regulatory model and addresses a host of new questions concerning the interplay between regulation and metabolism.
Abstract: This paper presents a new method, steady-state regulatory flux balance analysis (SR-FBA), for predicting gene expression and metabolic fluxes in a large-scale integrated metabolic‐regulatory model. Using SR-FBA to study the metabolism of Escherichia coli, we quantify the extent to which the different levels of metabolic and transcriptional regulatory constraints determine metabolic behavior: metabolic constraints determine the flux activity state of 45‐51% of metabolic genes, depending on the growth media, whereas transcription regulation determines the flux activity state of 13‐20% of the genes. A considerable number of 36 genes are redundantly expressed, that is, they are expressed even though the fluxes of their associated reactions are zero, indicating that they are not optimally tuned for cellular flux demands. The undetermined state of the remaining B30% of the genes suggests that they may represent metabolic variability within a given growth medium. Overall, SR-FBA enables one to address a host of new questions concerning the interplay between regulation and metabolism. Molecular Systems Biology 17 April 2007; doi:10.1038/msb4100141 Subject Categories: metabolic and regulatory networks; computational methods

Journal ArticleDOI
TL;DR: A novel computational approach is developed to identify microRNA genes induced by UV‐B radiation and characterize their functions in regulating gene expression and it is reported that in A. thaliana, 21 micro RNA genes in 11 microRNA families are upregulated underUV‐B stress condition.
Abstract: MicroRNAs (miRNAs) are small, non-coding RNAs that play critical roles in post-transcriptional gene regulation. In plants, mature miRNAs pair with complementary sites on mRNAs and subsequently lead to cleavage and degradation of the mRNAs. Many miRNAs target mRNAs that encode transcription factors; therefore, they regulate the expression of many downstream genes. In this study, we carry out a survey of Arabidopsis microRNA genes in response to UV-B radiation, an important adverse abiotic stress. We develop a novel computational approach to identify microRNA genes induced by UV-B radiation and characterize their functions in regulating gene expression. We report that in A. thaliana, 21 microRNA genes in 11 microRNA families are upregulated under UV-B stress condition. We also discuss putative transcriptional downregulation pathways triggered by the induction of these microRNA genes. Moreover, our approach can be directly applied to miRNAs responding to other abiotic and biotic stresses and extended to miRNAs in other plants and metazoans.

Journal ArticleDOI
TL;DR: This work combines traditional experiments with computational modeling, building a model that describes how stimulation of all four ErbB receptors with epidermal growth factor and heregulin leads to activation of two critical downstream proteins, extracellular‐signal‐regulated kinase (ERK) and Akt.
Abstract: Deregulation of ErbB signaling plays a key role in the progression of multiple human cancers. To help understand ErbB signaling quantitatively, in this work we combine traditional experiments with computational modeling, building a model that describes how stimulation of all four ErbB receptors with epidermal growth factor (EGF) and heregulin (HRG) leads to activation of two critical downstream proteins, extracellular-signal-regulated kinase (ERK) and Akt. Model analysis and experimental validation show that (i) ErbB2 overexpression, which occurs in approximately 25% of all breast cancers, transforms transient EGF-induced signaling into sustained signaling, (ii) HRG-induced ERK activity is much more robust to the ERK cascade inhibitor U0126 than EGF-induced ERK activity, and (iii) phosphoinositol-3 kinase is a major regulator of post-peak but not pre-peak EGF-induced ERK activity. Sensitivity analysis leads to the hypothesis that ERK activation is robust to parameter perturbation at high ligand doses, while Akt activation is not.

Journal ArticleDOI
TL;DR: It is shown that cellular responses to combinations of chemicals reveal how their biological targets are connected, and synergy profiles across many combinations show relatedness between targets in the whole network.
Abstract: Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.

Journal ArticleDOI
TL;DR: A novel computational method that uses an input–output hidden Markov model to model these regulatory networks while taking into account their dynamic nature is developed, leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress.
Abstract: Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein–DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input–output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.

Journal ArticleDOI
TL;DR: It is proposed that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes, which gave post‐WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose‐rich environments.
Abstract: After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD.

Journal ArticleDOI
TL;DR: This work presents PhosphoPep, a database containing more than 10 000 unique high‐confidence phosphorylation sites mapping to nearly 3500 gene models and 4600 distinct phosphoproteins of the Drosophila melanogaster Kc167 cell line, which constitutes the most comprehensive phosphorylated map of any single source to date.
Abstract: The ability to analyze and understand the mechanisms by which cells process information is a key question of systems biology research. Such mechanisms critically depend on reversible phosphorylation of cellular proteins, a process that is catalyzed by protein kinases and phosphatases. Here, we present PhosphoPep, a database containing more than 10 000 unique high-confidence phosphorylation sites mapping to nearly 3500 gene models and 4600 distinct phosphoproteins of the Drosophila melanogaster Kc167 cell line. This constitutes the most comprehensive phosphorylation map of any single source to date. To enhance the utility of PhosphoPep, we also provide an array of software tools that allow users to browse through phosphorylation sites on single proteins or pathways, to easily integrate the data with other, external data types such as protein-protein interactions and to search the database via spectral matching. Finally, all data can be readily exported, for example, for targeted proteomics approaches and the data thus generated can be again validated using PhosphoPep, supporting iterative cycles of experimentation and analysis that are typical for systems biology research.

Journal ArticleDOI
TL;DR: Is synthetic biology something really new or is it simply Biotechnology (another old and outdated buzzword) in new packaging?
Abstract: Mol Syst Biol. 3: 158 Life is evolving fast (at least in the first world) and the latest technological gadget becomes outdated even before we have learnt how to use it. In this respect, science is no exception. The new buzzword ‘Systems Biology’ entered the vocabulary of the scientific community only a few years ago. Now that every biologist is aware of it and almost everyone seems to be doing it, an even newer buzzword has entered the scientific arena, ‘Synthetic Biology’ (see as an example a sample of recent reviews on the topic, Benner and Sismour, 2005; Endy, 2005; Andrianantoandro et al , 2006; Heinemann and Panke, 2006). We are still arguing about the true definition of Systems Biology, the more so since it became fashionable for the funding agencies and therefore any research project should include it in the proposal, and now we need to define Synthetic Biology and explain to the non‐specialist what the difference is between the two. Is Synthetic Biology something really new or is it simply Biotechnology (another old and outdated buzzword) in new packaging? What are the achievements so far, what can we expect from it and are any biosafety dangers lurking ahead? How is Europe doing in this field compared with other countries? In this brief article we will attempt to provide some answers to those questions. A consensus definition drafted by a group of European experts defined Synthetic Biology as follows: ‘Synthetic biology is the engineering of biology: the synthesis of complex, biologically based (or inspired) systems, which display functions that do not exist in nature. This engineering perspective may be applied at all levels of the hierarchy of biological structures—from individual molecules to whole cells, tissues and organisms. In essence, synthetic biology will enable the …

Journal ArticleDOI
TL;DR: The development of a novel proteomic in vitro ubiquitination screen using a protein microarray platform that can be utilized for the discovery of substrates for E3 ligases on a global scale is described.
Abstract: Ubiquitin-protein ligases (E3s) are responsible for target recognition and regulate stability, localization or function of their substrates. However, the substrates of most E3 enzymes remain unknown. Here, we describe the development of a novel proteomic in vitro ubiquitination screen using a protein microarray platform that can be utilized for the discovery of substrates for E3 ligases on a global scale. Using the yeast E3 Rsp5 as a test system to identify its substrates on a yeast protein microarray that covers most of the yeast (Saccharomyces cerevisiae) proteome, we identified numerous known and novel ubiquitinated substrates of this E3 ligase. Our enzymatic approach was complemented by a parallel protein microarray protein interaction study. Examination of the substrates identified in the analysis combined with phage display screening allowed exploration of binding mechanisms and substrate specificity of Rsp5. The development of a platform for global discovery of E3 substrates is invaluable for understanding the cellular pathways in which they participate, and could be utilized for the identification of drug targets.

Journal ArticleDOI
TL;DR: The modular structure of the protein–protein interaction (PPI) networks during fruitfly and human brain aging and their relationships demonstrate that aging is largely associated with a small number, instead of many network modules, and that some modular changes might be reversible.
Abstract: Many fundamental questions on aging are still unanswered or are under intense debate. These questions are frequently not addressable by examining a single gene or a single pathway, but can best be addressed at the systems level. Here we examined the modular structure of the protein– protein interaction (PPI) networks during fruitfly and human brain aging. In both networks, there are two modules associated with the cellular proliferation to differentiation temporal switch that display opposite aging-related changes in expression. During fly aging, another couple of modules are associated with the oxidative–reductive metabolic temporal switch. These network modules and their relationships demonstrate (1) that aging is largely associated with a small number, instead of many network modules, (2) that some modular changes might be reversible and (3) that genes connecting different modules through PPIs are more likely to affect aging/longevity, a conclusion that is experimentally validated by Caenorhabditis elegans lifespan analysis. Network simulations further suggest that aging might preferentially attack key regulatory nodes that are important for the network stability, implicating a potential molecular basis for the stochastic nature of aging. Molecular Systems Biology 4 December 2007; doi:10.1038/msb4100189 Subject Categories: differentiation and death; molecular biology of disease

Journal ArticleDOI
TL;DR: The results indicate that the origin of DNA replication is the only vital, unique cis‐acting DNA sequence in the E. coli chromosome necessary for survival.
Abstract: The minimal set of genetic information necessary and sufficient to sustain a functioning cell contains not only trans-acting genes, but also cis-acting chromosomal regions that cannot be complemented by plasmids carrying these regions. In Escherichia coli (E. coli), only one chromosomal region, the origin of replication has been identified to be cis-acting. We constructed a series of mutants with long-range deletions, and the chromosomal regions containing trans-acting essential genes were deleted in the presence of plasmids complementing the deleted genes. The deleted regions cover all regions of the chromosome except for the origin and terminus of replication. The terminus affects cell growth, but is not essential. Our results indicate that the origin of DNA replication is the only vital, unique cis-acting DNA sequence in the E. coli chromosome necessary for survival.

Journal ArticleDOI
TL;DR: The systems perspective of this work views pathologies as hijackers of biological robustness and offers ways for identifying fragile hubs, as well as strategies to overcome drug resistance.
Abstract: Robust biological signaling networks evolved, through gene duplications, from simple, relatively fragile cascades. Architectural features such as layered configuration, branching and modularity, as well as functional characteristics (e.g., feedback control circuits), enable fail-safe performance in the face of internal and external perturbations. These universal features are exemplified here using the receptor tyrosine kinase (RTK) family. The RTK module is richly mutated and overexpressed in human malignancies, and pharmaceutical interception of its signaling effectively retards growth of specific tumors. Therapy-induced interception of RTK-signaling pathways and the common evolvement of drug resistance are respectively considered here as manifestations of fragility and plasticity of robust networks. The systems perspective we present views pathologies as hijackers of biological robustness and offers ways for identifying fragile hubs, as well as strategies to overcome drug resistance.

Journal ArticleDOI
TL;DR: It is shown that reverse‐engineered gene networks can be combined with expression profiles to compute the likelihood that genes and associated pathways are mediators of a disease and that the AR gene, in the context of the network, can be used as a marker to detect the aggressiveness of primary prostate cancers.
Abstract: Received 1.8.06; accepted 22.12.06 There is a need to identify genetic mediators of solid-tumor cancers, such as prostate cancer, where invasion and distant metastases determine the clinical outcome of the disease. Whole-genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by a condition from the hundreds to thousands of genes that exhibit changes in expression. Here, we show that reverse-engineered gene networks can be combined with expression profiles to compute the likelihood that genes and associated pathways are mediators of a disease. We apply our method to non-recurrent primary and metastatic prostate cancer data, and identify the androgen receptor gene (AR) among the top genetic mediators and the AR pathway as a highly enriched pathway for metastatic prostate cancer. These results were not obtained on the basis of expression change alone. We further demonstrate that the AR gene, in the context of the network, can be used as a marker to detect the aggressiveness of primary prostate cancers. This work shows that a network biology approach can be used advantageously to identify the genetic mediators and mediating pathways associated with a disease. Molecular Systems Biology 13 February 2007; doi:10.1038/msb4100125 Subject Categories: metabolic and regulatory networks; molecular biology of disease

Journal ArticleDOI
TL;DR: It is established that even duplicate pairs with compensation capacity exhibit rich and typically non‐overlapping deletion phenotypes, and are thus unable to comprehensively cover against loss of their paralog.
Abstract: Many genes can be deleted with little phenotypic consequences. By what mechanism and to what extent the presence of duplicate genes in the genome contributes to this robustness against deletions has been the subject of considerable interest. Here, we exploit the availability of high-density genetic interaction maps to provide direct support for the role of backup compensation, where functionally overlapping duplicates cover for the loss of their paralog. However, we find that the overall contribution of duplicates to robustness against null mutations is low ( approximately 25%). The ability to directly identify buffering paralogs allowed us to further study their properties, and how they differ from non-buffering duplicates. Using environmental sensitivity profiles as well as quantitative genetic interaction spectra as high-resolution phenotypes, we establish that even duplicate pairs with compensation capacity exhibit rich and typically non-overlapping deletion phenotypes, and are thus unable to comprehensively cover against loss of their paralog. Our findings reconcile the fact that duplicates can compensate for each other's loss under a limited number of conditions with the evolutionary instability of genes whose loss is not associated with a phenotypic penalty.