Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data

Background

Network inference deals with the reconstruction of molecular networks from experimental data. Given N molecular species, the challenge is to find the underlying network. Due to data limitations, this typically is an ill-posed problem, and requires the integration of prior biological knowledge or strong regularization. We here focus on the situation when time-resolved measurements of a system’s response after systematic perturbations are available.

Results

We present a novel method to infer signaling networks from time-course perturbation data. We utilize dynamic Bayesian networks with probabilistic Boolean threshold functions to describe protein activation. The model posterior distribution is analyzed using evolutionary MCMC sampling and subsequent clustering, resulting in probability distributions over alternative networks. We evaluate our method on simulated data, and study its performance with respect to data set size and levels of noise. We then use our method to study EGF-mediated signaling in the ERBB pathway.

Conclusions

Dynamic Probabilistic Threshold Networks is a new method to infer signaling networks from time-series perturbation data. It exploits the dynamic response of a system after external perturbation for network reconstruction. On simulated data, we show that the approach outperforms current state of the art methods. On the ERBB data, our approach recovers a significant fraction of the known interactions, and predicts novel mechanisms in the ERBB pathway.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-250) contains supplementary material, which is available to authorized users.     

Mirna expression profiles identify drivers in colorectal and pancreatic cancers

Background and Aim

Altered expression of microRNAs (miRNAs) hallmarks many cancer types. The study of the associations of miRNA expression profile and cancer phenotype could help identify the links between deregulation of miRNA expression and oncogenic pathways.

Methods

Expression profiling of 866 human miRNAs in 19 colorectal and 17 pancreatic cancers and in matched adjacent normal tissues was investigated. Classical paired t-test and random forest analyses were applied to identify miRNAs associated with tissue-specific tumors. Network analysis based on a computational approach to mine associations between cancer types and miRNAs was performed.

Results

The merge between the two statistical methods used to intersect the miRNAs differentially expressed in colon and pancreatic cancers allowed the identification of cancer-specific miRNA alterations. By miRNA-network analysis, tissue-specific patterns of miRNA deregulation were traced: the driving miRNAs were miR-195, miR-1280, miR-140-3p and miR-1246 in colorectal tumors, and miR-103, miR-23a and miR-15b in pancreatic cancers.

Conclusion

MiRNA expression profiles may identify cancer-specific signatures and potentially useful biomarkers for the diagnosis of tissue specific cancers. miRNA-network analysis help identify altered miRNA regulatory networks that could play a role in tumor pathogenesis.     

Microdosimetry: Principles and applications

Aim

to present the most important aspects of Microdosimetry, a research field in radiation biophysics.

Background

microdosimetry is the branch of radiation biophysics that systematically studies the spatial, temporal and spectral aspects of the stochastic nature of the energy deposition processes in microscopic structures.

Materials and Methods

we briefly review its history, the people, the formalism and the theories and devices that allowed researchers to begin to understand the true nature of radiation action on living matter.

Results and Conclusions

we outline some of its applications, especially to Boron Neutron Capture Therapy, attempting to explain the biological effectiveness of the boron thermal neutron capture reaction.     

Explicit Logic Circuits Predict Local Properties of the Neocortex's Physiology and Anatomy

Background

Two previous articles proposed an explicit model of how the brain processes information by its organization of synaptic connections. The family of logic circuits was shown to generate neural correlates of complex psychophysical phenomena in different sensory systems.

Methodology/Principal Findings

Here it is shown that the most cost-effective architectures for these networks produce correlates of electrophysiological brain phenomena and predict major aspects of the anatomical structure and physiological organization of the neocortex. The logic circuits are markedly efficient in several respects and provide the foundation for all of the brain''s combinational processing of information.

Conclusions/Significance

At the local level, these networks account for much of the physical structure of the neocortex as well its organization of synaptic connections. Electronic implementations of the logic circuits may be more efficient than current electronic logic arrays in generating both Boolean and fuzzy logic.     

Computational reconstruction of proteome-wide protein interaction networks between HTLV retroviruses and Homo sapiens

Background

Human T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported.

Results

In this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses.

Conclusions

The Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-245) contains supplementary material, which is available to authorized users.     

Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

Background

Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.

Results

Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G₁ fixed point was found for deterministic updating schemes.

Conclusions

In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.     

Protein Complex Identification by Integrating Protein-Protein Interaction Evidence from Multiple Sources

Background

Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification.

Results

We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks.

Conclusions

We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification.     

Scalable Steady State Analysis of Boolean Biological Regulatory Networks

Background

Computing the long term behavior of regulatory and signaling networks is critical in understanding how biological functions take place in organisms. Steady states of these networks determine the activity levels of individual entities in the long run. Identifying all the steady states of these networks is difficult due to the state space explosion problem.

Methodology

In this paper, we propose a method for identifying all the steady states of Boolean regulatory and signaling networks accurately and efficiently. We build a mathematical model that allows pruning a large portion of the state space quickly without causing any false dismissals. For the remaining state space, which is typically very small compared to the whole state space, we develop a randomized traversal method that extracts the steady states. We estimate the number of steady states, and the expected behavior of individual genes and gene pairs in steady states in an online fashion. Also, we formulate a stopping criterion that terminates the traversal as soon as user supplied percentage of the results are returned with high confidence.

Conclusions

This method identifies the observed steady states of boolean biological networks computationally. Our algorithm successfully reported the G1 phases of both budding and fission yeast cell cycles. Besides, the experiments suggest that this method is useful in identifying co-expressed genes as well. By analyzing the steady state profile of Hedgehog network, we were able to find the highly co-expressed gene pair GL1-SMO together with other such pairs.

Availability

Source code of this work is available at http://bioinformatics.cise.ufl.edu/palSteady.html twocolumnfalse]     

Cost-Effectiveness of a Community Pharmacist-Led Sleep Apnea Screening Program – A Markov Model

Background

Despite the high prevalence and major public health ramifications, obstructive sleep apnea syndrome (OSAS) remains underdiagnosed. In many developed countries, because community pharmacists (CP) are easily accessible, they have been developing additional clinical services that integrate the services of and collaborate with other healthcare providers (general practitioners (GPs), nurses, etc.). Alternative strategies for primary care screening programs for OSAS involving the CP are discussed.

Objective

To estimate the quality of life, costs, and cost-effectiveness of three screening strategies among patients who are at risk of having moderate to severe OSAS in primary care.

Design

Markov decision model.

Data Sources

Published data.

Target Population

Hypothetical cohort of 50-year-old male patients with symptoms highly evocative of OSAS.

Time Horizon

The 5 years after initial evaluation for OSAS.

Perspective

Societal.

Interventions

Screening strategy with CP (CP-GP collaboration), screening strategy without CP (GP alone) and no screening.

Outcomes measures

Quality of life, survival and costs for each screening strategy.

Results of base-case analysis

Under almost all modeled conditions, the involvement of CPs in OSAS screening was cost effective. The maximal incremental cost for “screening strategy with CP” was about 455€ per QALY gained.

Results of sensitivity analysis

Our results were robust but primarily sensitive to the treatment costs by continuous positive airway pressure, and the costs of untreated OSAS. The probabilistic sensitivity analysis showed that the “screening strategy with CP” was dominant in 80% of cases. It was more effective and less costly in 47% of cases, and within the cost-effective range (maximum incremental cost effectiveness ratio at €6186.67/QALY) in 33% of cases.

Conclusions

CP involvement in OSAS screening is a cost-effective strategy. This proposal is consistent with the trend in Europe and the United States to extend the practices and responsibilities of the pharmacist in primary care.     

Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

Background

Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized.

Methodology/Principal Findings

Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy.

Significance

Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure.     

Genetic diversity of selected genes that are potentially economically important in feral sheep of New Zealand

Background

Feral sheep are considered to be a source of genetic variation that has been lost from their domestic counterparts through selection.

Methods

This study investigates variation in the genes KRTAP1-1, KRT33, ADRB3 and DQA2 in Merino-like feral sheep populations from New Zealand and its offshore islands. These genes have previously been shown to influence wool, lamb survival and animal health.

Results

All the genes were polymorphic, but no new allele was identified in the feral populations. In some of these populations, allele frequencies differed from those observed in commercial Merino sheep and other breeds found in New Zealand. Heterozygosity levels were comparable to those observed in other studies on feral sheep. Our results suggest that some of the feral populations may have been either inbred or outbred over the duration of their apparent isolation.

Conclusion

The variation described here allows us to draw some conclusions about the likely genetic origin of the populations and selective pressures that may have acted upon them, but they do not appear to be a source of new genetic material, at least for these four genes.     

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks

Background

Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.

Methodology/Principal Findings

We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.

Conclusion/Significance

We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large-scale datasets.

Availability

Software and data sets are available at http://www.sfu.ca/~ester/software/DECOB.zip.     

Colored motifs reveal computational building blocks in the C. elegans brain

Background

Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network.

Methodology/Principal Findings

Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm''s locomotion.

Conclusions/Significance

The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available.     

Interference between sentence processing and probabilistic implicit sequence learning

Background

During sentence processing we decode the sequential combination of words, phrases or sentences according to previously learned rules. The computational mechanisms and neural correlates of these rules are still much debated. Other key issue is whether sentence processing solely relies on language-specific mechanisms or is it also governed by domain-general principles.

Methodology/Principal Findings

In the present study, we investigated the relationship between sentence processing and implicit sequence learning in a dual-task paradigm in which the primary task was a non-linguistic task (Alternating Serial Reaction Time Task for measuring probabilistic implicit sequence learning), while the secondary task were a sentence comprehension task relying on syntactic processing. We used two control conditions: a non-linguistic one (math condition) and a linguistic task (word processing task). Here we show that the sentence processing interfered with the probabilistic implicit sequence learning task, while the other two tasks did not produce a similar effect.

Conclusions/Significance

Our findings suggest that operations during sentence processing utilize resources underlying non-domain-specific probabilistic procedural learning. Furthermore, it provides a bridge between two competitive frameworks of language processing. It appears that procedural and statistical models of language are not mutually exclusive, particularly for sentence processing. These results show that the implicit procedural system is engaged in sentence processing, but on a mechanism level, language might still be based on statistical computations.