A framework for mapping, visualisation and automatic model creation of signal-transduction networks

An intuitive formalism for reconstructing cellular networks from empirical data is presented, and used to build a comprehensive yeast MAP kinase network. The accompanying rxncon software tool can convert networks to a range of standard graphical formats and mathematical models

Functional analysis of High-Throughput data for dynamic modeling in eukaryotic systems

Das Verhalten Biologischer Systeme wird durch eine Vielzahl regulatorischer Prozesse beeinflusst, die sich auf verschiedenen Ebenen abspielen. Die Forschung an diesen Regulationen hat stark von den großen Mengen von Hochdurchsatzdaten profitiert, die in den letzten Jahren verfügbar wurden. Um diese Daten zu interpretieren und neue Erkenntnisse aus ihnen zu gewinnen, hat sich die mathematische Modellierung als hilfreich erwiesen. Allerdings müssen die Daten vor der Integration in Modelle aggregiert und analysiert werden. Wir präsentieren vier Studien auf unterschiedlichen zellulären Ebenen und in verschiedenen Organismen. Zusätzlich beschreiben wir zwei Computerprogramme die den Vergleich zwischen Modell und Experimentellen Daten erleichtern. Wir wenden diese Programme in zwei Studien über die MAP Kinase (MAP, engl. mitogen-acticated-protein) Signalwege in Saccharomyces cerevisiae an, um Modellalternativen zu generieren und unsere Vorstellung des Systems an Daten anzupassen. In den zwei verbleibenden Studien nutzen wir bioinformatische Methoden, um Hochdurchsatz-Zeitreihendaten von Protein und mRNA Expression zu analysieren. Um die Daten interpretieren zu können kombinieren wir sie mit Netzwerken und nutzen Annotationen um Module identifizieren, die ihre Expression im Lauf der Zeit ändern. Im Fall der humanen somatischen Zell Reprogrammierung führte diese Analyse zu einem probabilistischen Boolschen Modell des Systems, welches wir nutzen konnten um neue Hypothesen über seine Funktionsweise aufzustellen. Bei der Infektion von Säugerzellen (Canis familiaris) mit dem Influenza A Virus konnten wir neue Verbindungen zwischen dem Virus und seinem Wirt herausfinden und unsere Zeitreihendaten in bestehende Netzwerke einbinden. Zusammenfassend zeigen viele unserer Ergebnisse die Wichtigkeit von Datenintegration in mathematische Modelle, sowie den hohen Grad der Verschaltung zwischen verschiedenen Regulationssystemen.The behavior of all biological systems is governed by numerous regulatory mechanisms, acting on different levels of time and space. The study of these regulations has greatly benefited from the immense amount of data that has become available from high-throughput experiments in recent years. To interpret this mass of data and gain new knowledge about studied systems, mathematical modeling has proven to be an invaluable method. Nevertheless, before data can be integrated into a model it needs to be aggregated, analyzed, and the most important aspects need to be extracted. We present four Systems Biology studies on different cellular organizational levels and in different organisms. Additionally, we describe two software applications that enable easy comparison of data and model results. We use these in two of our studies on the mitogen-activated-protein (MAP) kinase signaling in Saccharomyces cerevisiae to generate model alternatives and adapt our representation of the system to biological data. In the two remaining studies we apply Bioinformatic methods to analyze two high-throughput time series on proteins and mRNA expression in mammalian cells. We combine the results with network data and use annotations to identify modules and pathways that change in expression over time to be able to interpret the datasets. In case of the human somatic cell reprogramming (SCR) system this analysis leads to the generation of a probabilistic Boolean model which we use to generate new hypotheses about the system. In the last system we examined, the infection of mammalian (Canis familiaris) cells by the influenza A virus, we find new interconnections between host and virus and are able to integrate our data with existing networks. In summary, many of our findings show the importance of data integration into mathematical models and the high degree of connectivity between different levels of regulation

Rule-based Modeling of Cell Signaling: Advances in Model Construction, Visualization and Simulation

Rule-based modeling is a graph-based approach to specifying the kinetics of cell signaling systems. A reaction rule is a compact and explicit graph-based representation of a kinetic process, and it matches a class of reactions that involve identical sites and identical kinetics. Compact rule- based models have been used to generate large and combinatorially complex reaction networks, and rules have also been used to compile databases of kinetic interactions targeting specific cells and pathways. In this work, I address three technological challenges associated with rule-based modeling. First, I address the ability to generate an automated global visualization of a rule-based model as a network of signal flows. I showed how to analyze a reaction rule and extract a set of bipartite regulatory relationships, which can be aggregated across rules into a global network. I also provide a set of coarse-graining approaches to compress an automatically generated network into a compact pathway diagram, even for models with 100s of rules. Second, I resolved an incompatibility between two recent advances in rule-based modeling: network-free simulation (which enables simulation without generating a reaction network), and energy-based rule-based modeling (which enables specifying a model using cooperativity parameters and automated accounting of free energy). The incompatibility arose because calculating the reaction rate requires computing the reaction free energy in an energy-based model, and this requires knowledge of both reactants and products of the reaction, but the products are not available in a network-free simulation until after the reaction event has fired. This was resolved by expanding each energy- based rule into a number of normal reaction rules for which reaction free energies can be calculated unambiguously. Third, I demonstrated a particular type of modularization that is based on treating a set of rules as a module. This enables building models from combinations of modular hypotheses and supplements the other modularization strategies such as macros, types and energy-based compression

Applications of Boolean modelling to study and stratify dynamics of a complex disease

Interpretation of omics data is needed to form meaningful hypotheses about disease mechanisms. Pathway databases give an overview of disease-related processes, while mathematical models give qualitative and quantitative insights into their complexity. Similarly to pathway databases, mathematical models are stored and shared on dedicated platforms. Moreover, community-driven initiatives such as disease maps encode disease-specific mechanisms in both computable and diagrammatic form using dedicated tools for diagram biocuration and visualisation. To investigate the dynamic properties of complex disease mechanisms, computationally readable content can be used as a scaffold for building dynamic models in an automated fashion. The dynamic properties of a disease are extremely complex. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. In this study, Parkinson’s disease (PD) is analyzed as an example of a complex disorder. PD is associated with complex genetic, environmental causes and comorbidities that need to be analysed in a systematic way to better understand the progression of different disease subtypes. Studying PD as a multifactorial disease requires deconvoluting the multiple and overlapping changes to identify the driving neurodegenerative mechanisms. Integrated systems analysis and modelling can enable us to study different aspects of a disease such as progression, diagnosis, and response to therapeutics. Therefore, more research is required to better understand the complexity of molecular mechanisms, which may advance personalized medicine in the future. Modelling such complex processes depends on the scope and it may vary depending on the nature of the process (e.g. signalling vs metabolic). Experimental design and the resulting data also influence model structure and analysis. Boolean modelling is proposed to analyse the complexity of PD mechanisms. Boolean models (BMs) are qualitative rather than quantitative and do not require detailed kinetic information such as Petri nets or Ordinary Differential equations (ODEs). Boolean modelling represents a logical formalism where available variables have binary values of one (ON) or zero (OFF), making it a plausible approach in cases where quantitative details and kinetic parameters 9 are not available. Boolean modelling is well validated in clinical and translational medicine research. In this project, the PD map was translated into BMs in an automated fashion using different methods. Therefore, the complexity of disease pathways can be analysed by simulating the effect of genomic burden on omics data. In order to make sure that BMs accurately represent the biological system, validation was performed by simulating models at different scales of complexity. The behaviour of the models was compared with expected behavior based on validated biological knowledge. The TCA cycle was used as an example of a well-studied simple network. Different scales of complex signalling networks were used including the Wnt-PI3k/AKT pathway, and T-cell differentiation models. As a result, matched and mismatched behaviours were identified, allowing the models to be modified to better represent disease mechanisms. The BMs were stratified by integrating omics data from multiple disease cohorts. The miRNA datasets from the Parkinson’s Progression Markers Initiative study (PPMI) were analysed. PPMI provides an important resource for the investigation of potential biomarkers and therapeutic targets for PD. Such stratification allowed studying disease heterogeneity and specific responses to molecular perturbations. The results can support research hypotheses, diagnose a condition, and maximize the benefit of a treatment. Furthermore, the challenges and limitations associated with Boolean modelling in general were discussed, as well as those specific to the current study. Based on the results, there are different ways to improve Boolean modelling applications. Modellers can perform exploratory investigations, gathering the associated information about the model from literature and data resources. The missing details can be inferred by integrating omics data, which identifies missing components and optimises model accuracy. Accurate and computable models improve the efficiency of simulations and the resulting analysis of their controllability. In parallel, the maintenance of model repositories and the sharing of models in easily interoperable formats are also important

Phage--Bacteria Infection networks: from nestedness to modularity and back again

Bacteriophages (viruses that infect bacteria) are the most abundant biological life-forms on Earth. However, very little is known regarding the structure of phage-bacteria infections. In a recent study we showed that phage-bacteria infection assay datasets are statistically nested in small scale communities while modularity is not statistically present. We predicted that at large macroevolutionary scales, phage-bacteria infection assay datasets should be typified by a modular structure, even if there is nested structure at smaller scales. We evaluate and confirm this hypothesis using the largest study of the kind to date. The study in question represents a phage-bacteria infection assay dataset in the Atlantic Ocean region between the European continental shelf and the Sargasso Sea. We present here a digitized version of this study that consist of a bipartite network with 286 bacteria and 215 phages including 1332 positive interactions, together with an exhaustive structural analysis of this network. We evaluated the modularity and nestedness of the network and its communities using a variety of algorithms including BRIM (Bipartite, Recursively Induced Modules), NTC (Nestedness Temperature Calculator) and NODF (Nestedness Metric based on Overlap and Decreasing Filling). We also developed extensions of these standard methods to identify multi-scale structure in large phage-bacteria interaction datasets. In addition, we performed an analysis of the degree of geographical diversity and specialization among all the hosts and phages. We find that the largest-scale ocean dataset study, as anticipated by Flores et al. 2013, is highly modular and not significantly nested (computed in comparison to null models). More importantly is the fact that some of the communities extracted from Moebus and Nattkemper dataset were found to be nested. We examine the role of geography in driving these modular patterns and find evidence that phage-bacteria interactions can exhibit strong similarity despite large distances between sites. We discuss how models can help determine how coevolutionary dynamics between strains, within a site and across sites, drives the emergence of nested, modular and other complex phage-bacteria interaction networks. Finally, we releases a computational library (BiMAT)to help to help the ecology research community to perform bipartite network analysis of the same nature I did during my PhD.Ph.D

Glosarium Matematika

273 p.; 24 cm

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp