Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Keywords: Germany ; PATHWAYS ; NETWORKS ; SUPPORT ; SYSTEM ; ENZYMES ; GENE ; DRUG ; ACCURACY ; BIOLOGY ; TARGET ; MUTANT ; IDENTIFICATION ; ESCHERICHIA-COLI ; DATABASE ; US ; STRATEGIES ; OUTCOMES ; TARGETS ; ORGANIZATION ; RECONSTRUCTION ; FEATURES ; HOMOLOGY ; DRUG DISCOVERY ; ENZYME ; analysis ; HIGH-THROUGHPUT ; SCREEN ; MUTANTS ; ENGLAND ; MEDIA ; outcome ; Escherichia coli ; ESSENTIALITY ; GENOMIC INFORMATION
    Abstract: Background: Computational identification of new drug targets is a major goal of pharmaceutical bioinformatics. Results: This paper presents a machine learning strategy to study and validate essential enzymes of a metabolic network. Each single enzyme was characterized by its local network topology, gene homologies and co-expression, and flux balance analyses. A machine learning system was trained to distinguish between essential and non-essential reactions. It was validated by a comprehensive experimental dataset, which consists of the phenotypic outcomes from single knockout mutants of Escherichia coli (KEIO collection). We yielded very reliable results with high accuracy (93%) and precision (90%). We show that topologic, genomic and transcriptomic features describing the network are sufficient for defining the essentiality of a reaction. These features do not substantially depend on specific media conditions and enabled us to apply our approach also for less specific media conditions, like the lysogeny broth rich medium. Conclusion: Our analysis is feasible to validate experimental knockout data of high throughput screens, can be used to improve flux balance analyses and supports experimental knockout screens to define drug targets
    Type of Publication: Journal article published
    PubMed ID: 18652654
    Signatur Availability
    BibTip Others were also interested in ...
  • 2
    Keywords: RECEPTOR ; CELL ; Germany ; MODEL ; MODELS ; PATHWAY ; PATHWAYS ; NETWORKS ; SYSTEM ; SYSTEMS ; TRANSDUCTION ; ACTIVATION ; COMPLEX ; FAMILY ; REDUCTION ; BIOLOGY ; SIGNAL ; STIMULATION ; DESIGN ; PLASMA ; MEMBRANE ; PLASMA-MEMBRANE ; RECRUITMENT ; PROGENITOR CELLS ; sensitivity ; systems biology ; ERYTHROPOIETIN RECEPTOR ; ORDER ; FAMILIES ; INCREASE ; intensity ; CANDIDATE ; ENGLAND ; PREDICT ; AGREEMENT ; quantitative ; QUANTITATIVE DATA ; VALUES ; IDENTIFIABILITY ANALYSIS
    Abstract: Background: The amplification of signals, defined as an increase in the intensity of a signal through networks of intracellular reactions, is considered one of the essential properties in many cell signalling pathways. Despite of the apparent importance of signal amplification, there have been few attempts to formalise this concept. Results: In this work we investigate the amplification and responsiveness of the JAK2-STAT5 pathway using a kinetic model. The recruitment of EpoR to the plasma membrane, activation by Epo, and deactivation of the EpoR/JAK2 complex are considered as well as the activation and nucleocytoplasmic shuttling of STAT5. Using qualitative biological knowledge, we first establish the structure of a general power-law model. We then generate a family of models from which we select suitable candidates. The parameter values of the model are estimated from experimental quantitative time-course data. The final model, whether it is conventional model with fixed predefined integer kinetic orders or a model with variable non-integer kinetic orders, is selected on the basis of a good agreement between simulations and the experimental data. The model is used to analyse the responsiveness and amplification properties of the pathway with sustained, transient, and oscillatory stimulation. Conclusion: The selected kinetic model predicts that the system acts as an amplifier with maximum amplification and sensitivity for input signals whose intensity match physiological values for Epo concentration and with duration in the range of one to 100 minutes. The response of the system reaches saturation for more intense and longer stimulation with Epo. We hypothesise that these properties of the system directly relate to the saturation of Epo receptor activation, its low recruitment to the plasma membrane and intense deactivation as predicted by the model
    Type of Publication: Journal article published
    PubMed ID: 18439261
    Signatur Availability
    BibTip Others were also interested in ...
  • 3
    Abstract: Background: In breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer. Results: First, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation. Conclusion: In this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells
    Type of Publication: Journal article published
    PubMed ID: 19118495
    Signatur Availability
    BibTip Others were also interested in ...
  • 4
    Keywords: RECEPTOR ; PATHWAY ; ACTIVATION ; MAP KINASE ; INHIBITORS ; MAMMALIAN TARGET ; REGULATORY NETWORKS ; signaling networks ; TRASTUZUMAB RESISTANCE ; G1/S TRANSITION
    Abstract: BACKGROUND: Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations. Dimerisation with other receptors allows to bypass pathway blockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. We used longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplified breast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab or pertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modelling approach, signalling networks were reconstructed based on these data in a cell line and time course specific manner, including prior literature knowledge. Finally, we simulated network response to inhibitor combinations to detect signalling nodes reflecting growth inhibition. RESULTS: The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. In BT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPK signalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncovered feedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cell line. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT and p70S6K. The results indicated a pathway specific drug response, driven by the type of growth factor stimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumab inefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB and RPS6, relating to phenotypic events like cell growth or proliferation. Therefore, we additionally analysed RB and RPS6 in the long-term networks. CONCLUSIONS: We derived protein interaction models for three breast cancer cell lines. Changes compared to the common reference network hint towards individual characteristics and potential drug resistance mechanisms. Simulation of perturbations were consistent with the experimental data, confirming our combined reverse and forward engineering approach as valuable for drug discovery and personalised medicine.
    Type of Publication: Journal article published
    PubMed ID: 24970389
    Signatur Availability
    BibTip Others were also interested in ...
  • 5
    Keywords: RECEPTOR ; GROWTH ; GROWTH-FACTOR ; Germany ; MODEL ; PATHWAY ; PATHWAYS ; NETWORKS ; SYSTEM ; DISTINCT ; LIGAND ; MECHANISM ; mechanisms ; BIOLOGY ; GROWTH-FACTOR RECEPTOR ; STIMULATION ; NO ; SIGNAL-TRANSDUCTION ; EPIDERMAL-GROWTH-FACTOR ; DERIVATION ; signaling ; RE ; INCREASE ; LEVEL ; ENGLAND ; modeling ; modification ; VALUES ; CELL BIOLOGY ; EGF ; SUPPRESSES ; CLATHRIN-COATED PITS ; ENDOSOMES ; E5 ONCOPROTEIN ; MEDIATED ENDOCYTOSIS ; PROTEIN-KINASE CASCADE ; SWITCH-LIKE
    Abstract: Background: The Epidermal Growth Factor (EGF) receptor has been shown to internalize via clathrin-independent endocytosis (CIE) in a ligand concentration dependent manner. From a modeling point of view, this resembles an ultrasensitive response, which is the ability of signaling networks to suppress a response for low input values and to increase to a pre-defined level for inputs exceeding a certain threshold. Several mechanisms to generate this behaviour have been described theoretically, the underlying assumptions of which, however, have not been experimentally demonstrated for the EGF receptor internalization network. Results: Here, we present a mathematical model of receptor sorting into alternative pathways that explains the EGF-concentration dependent response of CIE. The described mechanism involves a saturation effect of the dominant clathrin-dependent endocytosis pathway and implies distinct steady-states into which the system is forced for low vs high EGF stimulations. The model is minimal since no experimentally unjustified reactions or parameter assumptions are imposed. We demonstrate the robustness of the sorting effect for large parameter variations and give an analytic derivation for alternative steady-states that are reached. Further, we describe extensibility of the model to more than two pathways which might play a role in contexts other than receptor internalization. Conclusion: Our main result is that a scenario where different endocytosis routes consume the same form of receptor corroborates the observation of a clear-cut, stimulus dependent sorting. This is especially important since a receptor modification discriminating between the pathways has not been found experimentally. The model is not restricted to EGF receptor internalization and might account for ultrasensitivity in other cellular contexts
    Type of Publication: Journal article published
    PubMed ID: 18394191
    Signatur Availability
    BibTip Others were also interested in ...
  • 6
    Keywords: CELL ; Germany ; PATHWAY ; INFORMATION ; NETWORK ; NETWORKS ; GENE ; GENES ; microarray ; DRUG ; BIOLOGY ; SEQUENCE ; SEQUENCES ; TARGET ; IDENTIFICATION ; MICROARRAY DATA ; ESCHERICHIA-COLI ; PREDICTION ; TARGETS ; PLASMODIUM-FALCIPARUM ; FEATURES ; RESOURCE ; MYCOBACTERIUM-TUBERCULOSIS ; DRUG DISCOVERY ; methods ; HIGH-THROUGHPUT ; SCREENS ; AREA ; Species ; ESSENTIAL GENES ; Sequence information ; ENTERICA SEROVAR TYPHIMURIUM ; PROTEIN-INTERACTION NETWORKS ; PSEUDOMONAS-AERUGINOSA ; SALMONELLA-ENTERICA
    Abstract: Background: Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective. Results: We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75%-81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway. Conclusions: Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism
    Type of Publication: Journal article published
    PubMed ID: 20438628
    Signatur Availability
    BibTip Others were also interested in ...
  • 7
    facet.materialart.
    facet.materialart.
    BMC Systems Biology 4 (), Art.Nr. 162- 
    Keywords: APOPTOSIS ; CELLS ; CELL ; CLASSIFICATION ; GENE ; GENE-EXPRESSION ; PROTEINS ; BIOLOGY ; chromosome ; BREAST-CANCER ; IDENTIFICATION ; PROGRESSION ; GENE-REGULATION ; COMMUNICATION ; MATHEMATICAL-THEORY ; PROTEIN REFERENCE DATABASE
    Abstract: Background: Formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. We wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample. Results: We investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. Proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network (high correlation = small distance). In cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. Proteins of high signaling frequency were enriched with cancer mutations. These proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells. Conclusion: Our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. From these cancer-specific regulation patterns novel signaling motifs are proposed
    Type of Publication: Journal article published
    PubMed ID: 21110851
    Signatur Availability
    BibTip Others were also interested in ...
  • 8
    Abstract: BACKGROUND: Analysis of genome-wide data is often carried out using standard methods such as differential expression analysis, clustering analysis and heatmaps. Beyond that, differential correlation analysis was suggested to identify changes in the correlation patterns between disease states. The detection of differential correlation is a demanding task, as the number of entries in the gene-by-gene correlation matrix is large. Currently, there is no gold standard for the detection of differential correlation and statistical validation. RESULTS: We developed two untargeted algorithms (DCloc and DCglob) that identify differential correlation patterns by comparing the local or global topology of correlation networks. Construction of networks from correlation structures requires fixing of a correlation threshold. Instead of a single cutoff, the algorithms systematically investigate a series of correlation thresholds and permit to detect different kinds of correlation changes at the same level of significance: strong changes of a few genes and moderate changes of many genes. Comparing the correlation structure of 208 ER- breast carcinomas and 208 ER+ breast carcinomas, DCloc detected 770 differentially correlated genes with a FDR of 12.8%, while DCglob detected 630 differentially correlated genes with a FDR of 12.1%. In two-fold cross-validation, the reproducibility of the list of the top 5% differentially correlated genes in 140 ER- tumors and in 140 ER+ tumors was 49% for DCloc and 33% for DCglob. CONCLUSIONS: We developed two correlation network topology based algorithms for the detection of differential correlations in different disease states. Clusters of differentially correlated genes could be interpreted biologically and included the marker genes hydroxyprostaglandin dehydrogenase (PGDH) and acyl-CoA synthetase medium chain 1 (ACSM1) of invasive apocrine carcinomas that were differentially correlated, but not differentially expressed. Using random subsampling and cross-validation, DCloc and DCglob were shown to identify specific and reproducible lists of differentially correlated genes.
    Type of Publication: Journal article published
    PubMed ID: 23945349
    Signatur Availability
    BibTip Others were also interested in ...
  • 9
    Abstract: BACKGROUND: Signal transduction pathways are important cellular processes to maintain the cell's integrity. Their imbalance can cause severe pathologies. As signal transduction pathways feature complex regulations, they form intertwined networks. Mathematical models aim to capture their regulatory logic and allow an unbiased analysis of robustness and vulnerability of the signaling network. Pathway detection is yet a challenge for the analysis of signaling networks in the field of systems biology. A rigorous mathematical formalism is lacking to identify all possible signal flows in a network model. RESULTS: In this paper, we introduce the concept of Manatee invariants for the analysis of signal transduction networks. We present an algorithm for the characterization of the combinatorial diversity of signal flows, e.g., from signal reception to cellular response. We demonstrate the concept for a small model of the TNFR1-mediated NF- kappaB signaling pathway. Manatee invariants reveal all possible signal flows in the network. Further, we show the application of Manatee invariants for in silico knockout experiments. Here, we illustrate the biological relevance of the concept. CONCLUSIONS: The proposed mathematical framework reveals the entire variety of signal flows in models of signaling systems, including cyclic regulations. Thereby, Manatee invariants allow for the analysis of robustness and vulnerability of signaling networks. The application to further analyses such as for in silico knockout was shown. The new framework of Manatee invariants contributes to an advanced examination of signaling systems.
    Type of Publication: Journal article published
    PubMed ID: 28754124
    Signatur Availability
    BibTip Others were also interested in ...
  • 10
    Keywords: CELLS ; PROTEIN ; BINDING ; VARIABILITY ; systems biology ; EXPORT ; FRAP ; STAT3 NUCLEAR IMPORT ; LATENT
    Abstract: Background: High-quality quantitative data is a major limitation in systems biology. The experimental data used in systems biology can be assigned to one of the following categories: assays yielding average data of a cell population, high-content single cell measurements and high-throughput techniques generating single cell data for large cell populations. For modeling purposes, a combination of data from different categories is highly desirable in order to increase the number of observable species and processes and thereby maximize the identifiability of parameters. Results: In this article we present a method that combines the power of high-content single cell measurements with the efficiency of high-throughput techniques. A calibration on the basis of identical cell populations measured by both approaches connects the two techniques. We develop a mathematical model to relate quantities exclusively observable by high-content single cell techniques to those measurable with high-content as well as high-throughput methods. The latter are defined as free variables, while the variables measurable with only one technique are described in dependence of those. It is the combination of data calibration and model into a single method that makes it possible to determine quantities only accessible by single cell assays but using high-throughput techniques. As an example, we apply our approach to the nucleocytoplasmic transport of STAT5B in eukaryotic cells. Conclusions: The presented procedure can be generally applied to systems that allow for dividing observables into sets of free quantities, which are easily measurable, and variables dependent on those. Hence, it extends the information content of high-throughput methods by incorporating data from high-content measurements.
    Type of Publication: Journal article published
    PubMed ID: 20687942
    Signatur Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...