Systematic analysis of time resolved high-throughput data using stochastic network inference methods

Breast Cancer is the most common cancer in women and is characterised by various deregulations in signalling processes, leading to abnormal proliferation, differentiation or apoptosis. Several treatments for breast cancer exist, including the human monoclonal antibody Trastuzumab and the small molecule erlotinib, which both target and inhibit receptors of the ERBB receptor network. However, signalling processes in cancers, especially under drug treatment are not yet completely understood, and methods that learn treatment specific regulation and signalling patterns on a system- wide view from experimental data are needed. One approach is the reconstruction of interaction networks for genes or proteins under external perturbation, and many different algorithms have been proposed in the past. These include Boolean networks, Bayesian Networks, Dynamic Bayesian networks and differential equation systems, all describing the system on a different level of accuracy and complexity. However, if external perturbation is applied, the targets of the perturbations either have to be known, or only the targets of a single perturbation can be learned directly from data in current algorithms. And in general, dependencies of signalling events at different time points should be included into the modelling frameworks, too. This work proposes a novel approach to learn networks from longitudinal and externally perturbed data, called Dynamic Deterministic Effects Propagation Networks (DDEPN )'. Nodes in the network correspond to genes or proteins, selected from a particular biological system, while edges describe the interactions between the nodes. DDEPN models the activity of a node as boolean variable (either active or passive) and creates an activity profile of all nodes for the given time frame, depending on a given network structure. The activity profile is assessed by a likelihood score that describes the probability of the measured data given the activity profile. A network structure that fits best the measured data is identified by modifying the network such that the likelihood score is optimised. DDEPN is applied to a phosphoproteomic dataset from the ERBB signalling cascade, as well as to gene expression data measuring cell cycle related genes. Known signalling cascades from the ERBB and cell cycle networks could be successfully reconstructed and DDEPN also outperformed related network inference approaches. Further, in the ERBB data set, the combined application of the drugs erlotinib and Trastuzumab to the breast cancer cell line HCC1954 resulted in potent inhibition of growth promoting signalling effects, reflected in the down-regulation of the MAPK and AKT signalling pathways. This suggests that this combination therapy could be also a promising option for treatment of breast cancer patients

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.Comment: 14 pages, 3 figures. Presented at the DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM), Sep 200

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

Pluripotent embryonic stem cells are of paramount importance for biomedical research thanks to their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory network. Latest advances in transcriptomics have made it possible to infer basic topologies of pluripotency governing networks. The inferred network topologies, however, only encode boolean information while remaining silent about the roles of dynamics and molecular noise in gene expression. These features are widely considered essential for functional decision making. Herein we developed a framework for extending the boolean level networks into models accounting for individual genetic switches and promoter architecture which allows mechanistic interrogation of the roles of molecular noise, external signaling, and network topology. We demonstrate the pluripotent state of the network to be a broad attractor which is robust to variations of gene expression. Dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the molecular noise originating from genetic switching events which makes cells more responsive to extracellular signals. Lastly we show that steady state probability landscape can be significantly remodeled by global gene switching rates alone which can be taken as a proxy for how global epigenetic modifications exert control over stability of pluripotent states.Comment: 11 pages, 7 figure

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

The inference of gene trees with species trees

Molecular phylogeny has focused mainly on improving models for the reconstruction of gene trees based on sequence alignments. Yet, most phylogeneticists seek to reveal the history of species. Although the histories of genes and species are tightly linked, they are seldom identical, because genes duplicate, are lost or horizontally transferred, and because alleles can co-exist in populations for periods that may span several speciation events. Building models describing the relationship between gene and species trees can thus improve the reconstruction of gene trees when a species tree is known, and vice-versa. Several approaches have been proposed to solve the problem in one direction or the other, but in general neither gene trees nor species trees are known. Only a few studies have attempted to jointly infer gene trees and species trees. In this article we review the various models that have been used to describe the relationship between gene trees and species trees. These models account for gene duplication and loss, transfer or incomplete lineage sorting. Some of them consider several types of events together, but none exists currently that considers the full repertoire of processes that generate gene trees along the species tree. Simulations as well as empirical studies on genomic data show that combining gene tree-species tree models with models of sequence evolution improves gene tree reconstruction. In turn, these better gene trees provide a better basis for studying genome evolution or reconstructing ancestral chromosomes and ancestral gene sequences. We predict that gene tree-species tree methods that can deal with genomic data sets will be instrumental to advancing our understanding of genomic evolution.Comment: Review article in relation to the "Mathematical and Computational Evolutionary Biology" conference, Montpellier, 201

Using single-cell genomics to understand developmental processes and cell fate decisions.

High-throughput -omics techniques have revolutionised biology, allowing for thorough and unbiased characterisation of the molecular states of biological systems. However, cellular decision-making is inherently a unicellular process to which "bulk" -omics techniques are poorly suited, as they capture ensemble averages of cell states. Recently developed single-cell methods bridge this gap, allowing high-throughput molecular surveys of individual cells. In this review, we cover core concepts of analysis of single-cell gene expression data and highlight areas of developmental biology where single-cell techniques have made important contributions. These include understanding of cell-to-cell heterogeneity, the tracing of differentiation pathways, quantification of gene expression from specific alleles, and the future directions of cell lineage tracing and spatial gene expression analysis.J.A.G. was supported by Wellcome Trust Grant “Systematic Identification of Lineage Specification in Murine Gastrulation” (109081/Z/15/A). A.S. was supported by Wellcome Trust Grant “Tracing early mammalian lineage decisions by single cell genomics” (105031/B/14/Z). J.C.M. was supported by core funding from Cancer Research UK (award no. A17197) and EMBL