66 research outputs found
Evolutionary Dynamics and Optimization: Neutral Networks as Model-Landscapes for RNA Secondary-Structure Folding-Landscapes
We view the folding of RNA-sequences as a map that assigns a pattern of base
pairings to each sequence, known as secondary structure. These preimages can be
constructed as random graphs (i.e. the neutral networks associated to the
structure ). By interpreting the secondary structure as biological
information we can formulate the so called Error Threshold of Shapes as an
extension of Eigen's et al. concept of an error threshold in the single peak
landscape. Analogue to the approach of Derrida & Peliti for a of the population
on the neutral network. On the one hand this model of a single shape landscape
allows the derivation of analytical results, on the other hand the concept
gives rise to study various scenarios by means of simulations, e.g. the
interaction of two different networks. It turns out that the intersection of
two sets of compatible sequences (with respect to the pair of secondary
structures) plays a key role in the search for ''fitter'' secondary structures.Comment: 20 pages, uuencoded compressed postscript-file, Proc. of ECAL '95
conference, to appear., email: chris @ imb-jena.d
k-PathA: k-shortest Path Algorithm
One important aspect of computational systems biology
includes the identification and analysis of functional response
networks within large biochemical networks. These functional
response networks represent the response of a biological system
under a particular experimental condition which can be used to
pinpoint critical biological processes.
For this purpose, we have developed a novel algorithm to calculate
response networks as scored/weighted sub-graphs spanned by
k-shortest simple (loop free) paths. The k-shortest simple path
algorithm is based on a forward/backward chaining approach
synchronized between pairs of processors. The algorithm scales
linear with the number of processors used. The algorithm
implementation is using a Linux cluster platform, MPI lam
and mpiJava messaging as well as the Java language for the
application.
The algorithm is performed on a hybrid human network consisting
of 45,041 nodes and 438,567 interactions together with
gene expression information obtained from human cell-lines
infected by influenza virus. Its response networks show the early
innate immune response and virus triggered processes within
human epithelial cells. Especially under the imminent threat of
a pandemic caused by novel influenza strains, such as the current
H1N1 strain, these analyses are crucial for a comprehensive
understanding of molecular processes during early phases of
infection. Such a systems level understanding may aid in the
identification of therapeutic markers and in drug development
for diagnosis and finally prevention of a potentially dangerous
disease
Identifying genes of gene regulatory networks using formal concept analysis
In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g. in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems
Algebraic comparison of meta bolic networks, phylogenetic inference, and metabolic innovation
Metabolic networks are naturally represented as directed hypergraphs in such a way that metabolites are nodes and enzyme-catalyzed reactions form (hyper)edges. The familiar operations from set algebra (union, intersection, and difference) form a natural basis for both the pairwise comparison of networks and identification of distinct metabolic features of a set of algorithms. We report here on an implementation of this approach and its application to the procaryotes. We demonstrate that metabolic networks contain valuable phylogenetic information by comparing phylogenies obtained from network comparisons with 16S RNA phylogenies. We then used the same software to study metabolic innovations in two sets of organisms, free living microbes and Pyrococci, as well as obligate intracellular pathogens
Algebraic comparison of metabolic networks, phylogenetic inference, and metabolic innovation
BACKGROUND: Comparison of metabolic networks is typically performed based on the organisms' enzyme contents. This approach disregards functional replacements as well as orthologies that are misannotated. Direct comparison of the structure of metabolic networks can circumvent these problems. RESULTS: Metabolic networks are naturally represented as directed hypergraphs in such a way that metabolites are nodes and enzyme-catalyzed reactions form (hyper)edges. The familiar operations from set algebra (union, intersection, and difference) form a natural basis for both the pairwise comparison of networks and identification of distinct metabolic features of a set of algorithms. We report here on an implementation of this approach and its application to the procaryotes. CONCLUSION: We demonstrate that metabolic networks contain valuable phylogenetic information by comparing phylogenies obtained from network comparisons with 16S RNA phylogenies. The algebraic approach to metabolic networks is suitable to study metabolic innovations in two sets of organisms, free living microbes and Pyrococci, as well as obligate intracellular pathogens
Response network analysis of differential gene expression in human epithelial lung cells during avian influenza infections
<p>Abstract</p> <p>Background</p> <p>The recent emergence of the H5N1 influenza virus from avian reservoirs has raised concern about future influenza strains of high virulence emerging that could easily infect humans. We analyzed differential gene expression of lung epithelial cells to compare the response to H5N1 infection with a more benign infection with Respiratory Syncytial Virus (RSV). These gene expression data are then used as seeds to find important nodes by using a novel combination of the Gene Ontology database and the Human Network of gene interactions. Additional analysis of the data is conducted by training support vector machines (SVM) with the data and examining the orientations of the optimal hyperplanes generated.</p> <p>Results</p> <p>Analysis of gene clustering in the Gene Ontology shows no significant clustering of genes unique to H5N1 response at 8 hours post infection. At 24 hours post infection, however, a number of significant gene clusters are found for nodes representing "immune response" and "response to virus" terms. There were no significant clusters of genes in the Gene Ontology for the control (Mock) or RSV experiments that were unique relative to the H5N1 response. The genes found to be most important in distinguishing H5N1 infected cells from the controls using SVM showed a large degree of overlap with the list of significantly regulated genes. However, though none of these genes were members of the GO clusters found to be significant.</p> <p>Conclusions</p> <p>Characteristics of H5N1 infection compared to RSV infection show several immune response factors that are specific for each of these infections. These include faster timescales within the cell as well as a more focused activation of immunity factors. Many of the genes that are found to be significantly expressed in H5N1 response relative to the control experiments are not found to cluster significantly in the Gene Ontology. These genes are, however, often closely linked to the clustered genes through the Human Network. This may suggest the need for more diverse annotations of these genes and verification of their action in immune response.</p
Tox2 is required for the maintenance of GC TFH cells and the generation of memory TFH cells
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čŚăŞTăŞăłăçĺ ĺăŽçşčŚ. 亏é˝ĺ¤§ĺŚăăŹăšăŞăŞăźăš. 2021-10-15.Molecules for building stronger antibodies. 亏é˝ĺ¤§ĺŚăăŹăšăŞăŞăźăš. 2021-10-15.Memory T follicular helper (TFH) cells play an essential role to induce secondary antibody response by providing help to memory and naĂŻve B cells. Here, we show that the transcription factor Tox2 is vital for the maintenance of TFH cells in germinal centers (GCs) and the generation of memory TFH cells. High Tox2 expression was almost exclusive to GC TFH cells among human tonsillar and blood CD4+ T cell subsets. Tox2 overexpression maintained the expression of TFH-associated genes in T cell receptorâstimulated human GC TFH cells and inhibited their spontaneous conversion into TH1-like cells. Tox2-deficient mice displayed impaired secondary TFH cell expansion upon reimmunization with an antigen and upon secondary infection with a heterologous influenza virus. Collectively, our study shows that Tox2 is highly integrated into establishment of durable GC TFH cell responses and development of memory TFH cells in mice and humans
Defining genes: a computational framework
The precise elucidation of the gene concept has become the subject of intense discussion in light of results from several, large high-throughput surveys of transcriptomes and proteomes. In previous work, we proposed an approach for constructing gene concepts that combines genomic heritability with elements of function. Here, we introduce a definition of the gene within a computational framework of cellular interactions. The definition seeks to satisfy the practical requirements imposed by annotation, capture logical aspects of regulation, and encompass the evolutionary property of homology
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