Inferring qualitative relations in genetic networks and metabolic pathways

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.     

Gene networks inference using dynamic Bayesian networks

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.     

Biomedical question answering using semantic relations

Background

The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature.

Results

We extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%).

Conclusions

In this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0365-3) contains supplementary material, which is available to authorized users.     

Combinatorial Contextualization of Peptidic Epitopes for Enhanced Cellular Immunity

Invocation of cellular immunity by epitopic peptides remains largely dependent on empirically developed protocols, such as interfusion of aluminum salts or emulsification using terpenoids and surfactants. To explore novel vaccine formulation, epitopic peptide motifs were co-programmed with structural motifs to produce artificial antigens using our “motif-programming” approach. As a proof of concept, we used an ovalbumin (OVA) system and prepared an artificial protein library by combinatorially polymerizing MHC class I and II sequences from OVA along with a sequence that tends to form secondary structures. The purified endotoxin-free proteins were then examined for their ability to activate OVA-specific T-cell hybridoma cells after being processed within dendritic cells. One clone, F37A (containing three MHC I and two MHC II OVA epitopes), possessed a greater ability to evoke cellular immunity than the native OVA or the other artificial antigens. The sensitivity profiles of drugs that interfered with the F37A uptake differed from those of the other artificial proteins and OVA, suggesting that alteration of the cross-presentation pathway is responsible for the enhanced immunogenicity. Moreover, F37A, but not an epitopic peptide, invoked cellular immunity when injected together with monophosphoryl lipid A (MPL), and retarded tumor growth in mice. Thus, an artificially synthesized protein antigen induced cellular immunity in vivo in the absence of incomplete Freund''s adjuvant or aluminum salts. The method described here could be potentially used for developing vaccines for such intractable ailments as AIDS, malaria and cancer, ailments in which cellular immunity likely play a crucial role in prevention and treatment.     

MedEvi: retrieving textual evidence of relations between biomedical concepts from Medline

Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results. AVAILABILITY: http://www.ebi.ac.uk/tc-test/textmining/medevi/     

Assessing rbf networks using DELVE

In this paper, different methods for training radial basis function (RBF) networks for regression problems are described and illustrated. Then, using data from the DELVE archive, they are empirically compared with each other and with some other well known methods for machine learning. Each of the RBF methods performs well on at least one DELVE task, but none are as consistent as the best of the other non-RBF methods.     

Adaptive networks using learning matrices

Summary The performance of the Learning Matrix (LM) is suitable for the design of adaptive networks of higher complexity. It has been published, how to connect a LM with a generator of patterns (binary or nonbinary) and a ring-counter to result in an automatic classification of the presented patterns. This paper describes, how to connect two LM's to form an Autonomous Learning Matrix Dipole (ALD) and how to organize it, so that it adapts itself to an environment according to a given evaluation scale. For this purpose, a third type of input (beside e and b), namely h seems to be useful. This h-input controls the rate of adaptation of the LM.Using such ALD's, one may design adaptive structures of even higher complexity, for example with an adaptive internal model.The principle of Learning Matrices has been explained in detail (see e.g. IEEE Transactions on Electronic Computers, Vol. EC-12, No. 6, December, 1963, pp. 846–862). Using such learning matrices (LM), one may build up adaptive networks with rather interesting functions. Perhaps they are interesting for the physiologist and psychologist as well as for the engineer. Let us first recall the most essential details of the LM's.

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Zusammenfassung Die Funktion der Lernmatrix (LM) erlaubt den Entwurf adaptiver Netzwerke höherer Komplexität. Es wurde an anderer Stelle schon beschrieben, wie eine LM (binär oder nichtbinär) mit einem Generator für Eigenschaftssätze und einem Ringzähler zusammengeschaltet werden kann, um eine selbsttätige Klassifikation der angebotenen Eigenschaftssätze zu bewirken. Im vorliegenden Aufsatz wird erklärt, wie zwei LM so zusammengeschaltet werden können, dacß sich ein Autonomer Lernmatrix-Dipol (ALD) ergibt, und wie dieser zu organisieren ist, daß er sich einer gegebenen Außenwelt nach Maßgabe einer vorgegebenen Werteskala anpaßt. Zu diesem Zweck erweist sich außer den bisher beschriebenen beiden Zugangen zur LM (nämlich e und b) ein dritter sehr zweckmäßig, nämlich h. Dieser h-Eingang beeinflußt die Lerngeschwindigkeit der LM.Unter Verwendung solcher ALD's kann man adaptive Strukturen noch höherer Komplexität aufbauen, beispielsweise solche mit adaptivem innerem Modell.

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Visiting Professor of Electrical Engineering Stanford University.     

Nicotiana tabacum protoplasts secretome can evidence relations among regulatory elements of exocytosis mechanisms

An alternative study involving proteome analysis of the 24 h Nicotiana tabacum protoplast culture medium was performed with the aim to confirm relations among regulatory elements of exocytotic processes. Protoplasts present many convenient features to study cellular processes during transient overexpression or suppression of specific gene''s products.We performed a proteomic analysis of the culture medium fraction of protoplasts transiently expressing transgenes for 24 h to characterize the effect of various regulatory proteins dominant negative mutants.A total number of 49 spots were found reproducible in the medium. Twenty-four of these spots were identified with nano RP-HPLC-ESI-MS/MS. Only three and six spots were respectively identified as canonical and non-canonical secreted cell wall proteins.The low number of spots present in the culture medium fraction allowed us the ambitious experiment to analyze the influence of various SNAREs (SYP121, SYP122, SNAP33) and Rab (Rab11) dominant negative mutants. Missing a reasonable number of identified proteins the analyses gave rise to a similarity matrix statistically analyzed considering variation within the presence of 24 spots reproducible in presence of transient overexpression of SNAREs (SYP121 and SYP122) and Rab11 native cDNAs. The similarity confirmed the closer relation between the function of SYP122 and Rab11 as evidenced by the secRGUS based analysis. This analysis included the effect of SNAP33 DN mutant and showed that this Q_b–c-SNARE influence both SYP121 and SYP122 SNARE complexes.Key words: secretome, protoplast, secretion, SNARE, GUS, SNAP33     

No evidence that consistent auditory cues facilitate learning of spatial relations among locations

Human participants searched in a dynamic three-dimensional computer-generated virtual-environment open-field search task for four hidden goal locations arranged in a diamond configuration located in a 5×5 matrix of raised bins. Participants were randomly assigned to one of two groups: Consistent or Inconsistent. All participants experienced 30 trials in which four goal locations maintained the same spatial relations to each other (i.e., a diamond pattern), but this diamond pattern moved to random locations within the 5×5 matrix from trial-to-trial. For participants in the Consistent group, each goal location within the pattern always provided a unique and consistent auditory cue throughout the experimental session. For participants in the Inconsistent group, the same distinct auditory cues were provided for each goal location; however, the locations of these auditory cues within the pattern itself were randomized from trial-to-trial throughout the experimental session. Results indicated that participants in both groups learned the spatial configuration of goal locations, but the presence of consistent auditory cues did not facilitate the learning of spatial relations among locations.     

The emergence of abstract ideas: evidence from networks and babies

What is abstraction? In our view, abstraction is generalization. Specifically, we propose that abstract concepts emerge as the natural product of associative learning and generalization by similarity. We support this proposal by presenting evidence for two ideas: first, that children''s knowledge about how categories are organized and how words refer to them can be explained as learned generalizations over specific experiences of words referring to categories; and second, that the path of concepts from concrete to more abstract can be observed throughout development and that even in their more abstract form, concepts retain some of their original sensory basis. We illustrate these two facts by examining, in two kinds of learners--networks and young children--the development of three abstract ideas: (i) the idea of word; (ii) the idea of object; and (iii) the idea of substance.     

Modeling of pain using artificial neural networks

In dealing with human nervous system, the sensation of pain is as sophisticated as other physiological phenomena. To obtain an acceptable model of the pain, physiology of the pain has been analysed in the present paper. Pain mechanisms are explained in block diagram representation form. Because of the nonlinear interactions existing among different sections in the diagram, artificial neural networks (ANNs) have been exploited. The basic patterns associated with chronic and acute pain have been collected and then used to obtain proper features for training the neural networks. Both static and dynamic representations of the ANNs were used in this regard. The trained networks then were employed to predict response of the body when it is exposed to special excitations. These excitations have not been used in the training phase and their behavior is interesting from the physiological view. Some of these predictions can be inferred from clinical experimentations. However, more clinical tests have to be accomplished for some of the predictions.     

Dynamic simulation of regulatory networks using SQUAD

Background  

The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology.     

Study of biological networks using graph theory

As an effective modeling, analysis and computational tool, graph theory is widely used in biological mathematics to deal with various biology problems. In the field of microbiology, graph can express the molecular structure, where cell, gene or protein can be denoted as a vertex, and the connect element can be regarded as an edge. In this way, the biological activity characteristic can be measured via topological index computing in the corresponding graphs. In our article, we mainly study the biology features of biological networks in terms of eccentric topological indices computation. By means of graph structure analysis and distance calculating, the exact expression of several important eccentric related indices of hypertree network and X-tree are determined. The conclusions we get in this paper illustrate that the bioengineering has the promising application prospects.     

Redesigning metabolic networks using mathematical programming

The design of new generation bioprocessing plants is increasingly dependent on the design of process-compatible microorganisms. The latter, whether through genetic or physiological manipulations, can be greatly assisted by metabolic engineering. An emerging powerful tool in metabolic engineering research is computer-assisted cell design using mathematical programming. In this work, the problem of optimizing cellular metabolic networks has been formulated as a Mixed Integer Nonlinear Programming (MINLP) model. The model can assist genetic engineers to identify which cellular enzymes should be modified, and the new levels of activity required to produce an optimal network. Results are presented from the tricarboxylic acid cycle in Dictyostelium discoideum. Copyright 1998 John Wiley & Sons, Inc.     

Large-scale genetic epistasis networks using RNAi

Pairwise quantitative genetic interactions are mapped by combinatorial RNA interference in metazoan cells.