Reverse engineering of biological signaling networks via integration of data and knowledge using probabilistic graphical models

Motivation The postulate that biological molecules rather act together in intricate networks, pioneered systems biology and popularized the study on approaches to reconstruct and understand these networks. These networks give an insight of the underlying biological process and diseases involving aberration in these pathways like, cancer and neuro degenerative diseases. These networks can be reconstructed by two different approaches namely, data driven and knowledge driven methods. This leaves a critical question of relying on either of them. Relying completely on data driven approaches brings in the issue of overfitting, whereas, an entirely knowledge driven approach leaves us without acquisition of any new information/knowledge. This thesis presents hybrid approach in terms of integration of high throughput data and biological knowledge to reverse-engineer the structure of biological networks in a probabilistic way and showcases the improvement brought about as a result. Accomplishments The current work aims to learn networks from perturbation data. It extends the existing Nested Effects Model (NEMs) for pathway reconstruction in order to use the time course data, allowing the differentiation between direct and indirect effects and resolve feedback loops. The thesis also introduces an approach to learn the signaling network from phenotype data in form of images/movie, widening the scope of NEMs, which was so far limited to gene expression data. Furthermore, the thesis introduces methodologies to integrate knoowledge from different existing sources as probabilistic prior that improved the reconstruction accuracy of the network and could make it biologically more rational. These methods were finally integrated and for reverse engineering of more accurate and realistic networks. Conclusion The thesis added three dimensions to existing scope of network reverse engineering specially Nested Effects Models in terms of use of time course data, phenotype data and finally the incorporation of prior biological knowledge from multiple sources. The approaches developed demonstrate their application to understand signaling in stem cells and cell division and breast cancer. Furthermore the integrative approach shows the reconstruction of AMPK/EGFR pathway that is used to identify potential drug targets in lung cancer which were also validated experimentally, meeting one of the desired goals in systems biology

The role of breast-feeding in infant immune system: a systems perspective on the intestinal microbiome

BACKGROUND: The human intestinal microbiota changes from being sparsely populated and variable to possessing a mature, adult-like stable microbiome during the first 2 years of life. This assembly process of the microbiota can lead to either negative or positive effects on health, depending on the colonization sequence and diet. An integrative study on the diet, the microbiota, and genomic activity at the transcriptomic level may give an insight into the role of diet in shaping the human/microbiome relationship. This study aims at better understanding the effects of microbial community and feeding mode (breast-fed and formula-fed) on the immune system, by comparing intestinal metagenomic and transcriptomic data from breast-fed and formula-fed babies.RESULTS: We re-analyzed a published metagenomics and host gene expression dataset from a systems biology perspective. Our results show that breast-fed samples co-express genes associated with immunological, metabolic, and biosynthetic activities. The diversity of the microbiota is higher in formula-fed than breast-fed infants, potentially reflecting the weaker dependence of infants on maternal microbiome. We mapped the microbial composition and the expression patterns for host systems and studied their relationship from a systems biology perspective, focusing on the differences.CONCLUSIONS: Our findings revealed that there is co-expression of more genes in breast-fed samples but lower microbial diversity compared to formula-fed. Applying network-based systems biology approach via enrichment of microbial species with host genes revealed the novel key relationships of the microbiota with immune and metabolic activity. This was supported statistically by data and literature

Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources

<div><p>Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.</p></div

Plot showing the balanced accuracies of networks with varying number of nodes (20, 40 and 60) created just from different kinds of prior knowledge.

<p>The networks were extracted from KEGG via random walks. The plot shows the effect of size of network of different priors and also compares them to the knowledge from STRING database.</p

Network reconstruction for the breast cancer data (van't Veer et.al.).

<p>(<b>a</b>) The reconstructed network from data without using any prior. (<b>b</b>) Reconstructed network using the NOM prior. Black edges in the network could be verified with established literature knowledge, whereas the grey edges could not be verified. (<b>c</b>) The plot shows the edge recovery of the network from two points of view points: knowledge view = literature network mapped onto reconstructed network; model view = reconstructed edges mapped onto literature network.</p

Yeast (<i>Saccharomyces cerevisiae</i>) heat-shock response network obtained via Bayesian network reconstruction.

<p>(a) Network without any prior knowledge, (b) The gold standard network from YEASTRACT database (c) Network reconstructed with prior knowledge (here: NOM).</p

Optimally balanced accuracy for reconstructing networks from simulated categorical data with different kinds of prior (# nodes = 10).

<p>Optimally balanced accuracy for reconstructing networks from simulated categorical data with different kinds of prior (# nodes = 10).</p

Graphical models representing approaches.

<p>(a) A general Latent Factor Model (LFM). The random variables , and are highly related variables (left) and an assumption that these related random variables originate from a common, true but unknown variable results a bayesian network (right) in case of networks is the true but unknown network. <b>(b)</b> A generalized view of a Noisy-OR model showing the relation between causes and effect through a Noisy-OR function.</p

Reconstruction performance of Yeast (<i>Saccharomyces cerevisiae</i>) heat-shock response network with Bayesian Networks and different priors (NP = No Prior).

<p>Reconstruction performance of Yeast (<i>Saccharomyces cerevisiae</i>) heat-shock response network with Bayesian Networks and different priors (NP = No Prior).</p