Gene Expression Prediction by Soft Integration and the Elastic Net—Best Performance of the DREAM3 Gene Expression Challenge

Background: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance. Methodology/Principal Findings: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance. Conclusions/Significance: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.CENII

Logistik - ohne Zeitwirtschaft nicht denkbar : Zeitfaktor bestimmt wirtschaftlichen Ressourceneinsatz

Standen bisher bei den umfassend in der Literatur behandelten Logistikansätzen mehr die Materialwirtschaftsprobleme hinsichtlich der zeitgerechten Beschaffung und der Bestandshöhe im Vordergrund der Betrachtung, so meldet sich jetzt die Zeitwirtschaft mit den damit verbundenen Anstößen auf die Unternehmensstrategie zurück. Der Zeitfaktor ist bei kapitalintensiver Produktion schon immer ein knappes Gut gewesen, mit dem gut gewirtschaftet werden musste, um wettbewerbsfähig zu bleiben. Die derzeitige Wettbewerbssituation mit immer kürzeren Produkt-Entwicklungszeiten, Produkt-Lebenszeiten, Produkt-Herstellzeiten, Produkt-Lieferzeiten verstärkt die enorme Bedeutung des Zeitfaktors als Wert oder Bezugsgröße für Kosten, Termine oder Kapazitäten

The Influence of Costimulation and Regulatory Cd4+ T Cells on Intestinal Iga Immune Responses

It is thought that IgA B-cell differentiation is highly dependent on activated CD4+ T cells. In particular, cell-cell interactions in the Peyer's patches involving CD40 and/or CD80/CD86 have been implicated in germinal-center formation and IgA B-cell development. Also soluble factors, such as IL-4, IL-5, IL-6, and TGFβ may be critical for IgA B-cell differentiation in vivo. Here we report on some paradoxical findings with regard to IgA B-cell differentiation and specific mucosal immune responses that we have recently made using gene knockout mice. More specifically, we have investigated to what extent absence of CD4+ T cells, relevant cytokines, or T-cell-B-cell interactions would influence IgA B-cell differentiation in vivo. Using CD4– or IL- 4-gene knockout mice or mice made transgenic for CTLA4Ig, we found that, although specific responses were impaired, total IgA production and IgA B-cell differentiation appeared to proceed normally. However, a poor correlation was found between, on the one hand, GC formation and IgA differentiation and, on the other hand, the ability to respond to T-celldependent soluble protein antigens in these mice. Thus, despite the various deficiencies in CD4+ T-cell functions seemingly intact IgA B-cell development was observed

Comparison and validation of community structures in complex networks

The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.Comment: To appear in Physica A; 25 page

Stability and Flexibility from a System Analysis of Gene RegulatoryNetworks Based on Ordinary Differential Equations

The inference of large-scale gene regulatory networks from high-throughput data sets has revealed a diverse picture of only partially overlapping descriptions. Nevertheless, several properties in the organization of these networks are recurrent, such as hubs, a modular structure and certain motifs. Several authors have recently claimed cell systems to be stable against perturbations and random errors, but still able to rapidly switch between different states from specific stimuli. Since inferred mathematical models of large-scale systems need to be extremely simple to avoid overfitting, these two features are hard to attain simultaneously for a model. Here we review and discuss possible measures of how system stability and flexibility may be manifested and measured for linearized models based on systems of ordinary differential equations. Furthermore, we review how the network properties mentioned above together with the nature of the interactions contribute to these systems level properties. It turns out that the presence of repressed hubs, together with other phenomena of topological nature such as motifs and modules, contribute to the overall stability and/or flexibility of the model

Spearman rank correlation for each time point.

<p>The correlations are all with respect to the gold standard. The upper blue curve (stars) is our result; the green curve slightly below (rings) belongs to Ruan <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009134#pone.0009134-Ruan1" target="_blank">[11]</a>, while the lower red curve (plus-signs) is the mean of all other participants. The connecting lines are only guides for the eye. Note how the rankings for some time points obviously are harder to predict than others, and that the results are clearly co-varying.</p

Spearman rank correlations based on two different inference schemes.

<p>The correlations are based on cross-validations, where the last column stands for an overall calculation based on 24 ranking lists. A minimization of least squares, combined with a penalty term of the form of the elastic net, gives the best performance.</p

Spearman rank correlations after soft integration of other data sets.

<p>The expression data are obtained from the Rosetta Inpharmatics and ncbi omnibus, and integrated into the inference process by more terms in the objective function. The TF-binding data come from Yeastract and form priors for the penalty term, making it more probable that genes which are co-regulated should act as predictors for each other. Both data sets are only included to the extent the cross-validation procedure allows.</p

Spearman rank correlations for predictions obtained by perfect fits and minimization of L1- and L2-norms.

<p>The correlations are based on cross-validations, where the last column stands for an overall calculation based on 24 ranking lists. The minimization with respect to an L2-norm has the best performance, both for including only expression values and for including both expression values and rates. Following the principle of including as little as possible, we discard the rates.</p