Inference in systems biology: modelling approaches and applications

The main topic of this thesis is the study of biological regulatory systems using different computational modelling approaches in order to gain new insights into not yet completely understood biological processes. In "systems biology", mathematical models represent a powerful tool to study biological processes. Models are abstractions of reality always including some degree of simplification: an important ingredient of the modelling process, having a major role in suggesting the appropriate level of abstraction and simplification, is the purpose of the model, that is the question they have to answer. This thesis is focused on the analysis of how models of different complexity appropriately describe the available data to achieve a given purpose. Such analysis guides the choice of the most appropriate degree of simplification of the system under study that allows neglecting some aspects without compromising the results of the model. Three levels of detail for inference and modelling are analyzed in this thesis depending on the system under consideration. The first level is the network level, where molecules are nodes connected by edges and the interest is in the inference of the topology of connections at large scale. In the second level the network is interpreted as a mean to produce qualitative simulations and predictions which can be compared with experimental data. The third level of detail consist in a more mechanistic dynamic description of the system using ordinary differential equations but limiting the analysis to small subsystems. For each level of detail, appropriate approaches have been developed and applied to in silico and real data of different biological systems. Finally, different modelling appraches have been integrated to analyze insulin signalling pathway on different levels of simplification using a novel experimental dataset collected specifically for this purpos

Multi-Omics Profiling of the Tumor Microenvironment: Paving the Way to Precision Immuno-Oncology

The tumor microenvironment (TME) is a multifaceted ecosystem characterized by profound cellular heterogeneity, dynamicity, and complex intercellular cross-talk. The striking responses obtained with immune checkpoint blockers, i.e., antibodies targeting immune-cell regulators to boost antitumor immunity, have demonstrated the enormous potential of anticancer treatments that target TME components other than tumor cells. However, as checkpoint blockade is currently beneficial only to a limited fraction of patients, there is an urgent need to understand the mechanisms orchestrating the immune response in the TME to guide the rational design of more effective anticancer therapies. In this Mini Review, we give an overview of the methodologies that allow studying the heterogeneity of the TME from multi-omics data generated from bulk samples, single cells, or images of tumor-tissue slides. These include approaches for the characterization of the different cell phenotypes and for the reconstruction of their spatial organization and inter-cellular cross-talk. We discuss how this broader vision of the cellular heterogeneity and plasticity of tumors, which is emerging thanks to these methodologies, offers the opportunity to rationally design precision immuno-oncology treatments. These developments are fundamental to overcome the current limitations of targeted agents and checkpoint blockers and to bring long-term clinical benefits to a larger fraction of cancer patients

A rule-based model of insulin signalling pathway

BACKGROUND: The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as “combinatorial complexity”, which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions. RESULTS: In this work, we present a comprehensive model of the ISP, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways. After RBM specification, we simulated the dynamic behaviour of the ISP model and validated it using experimental data. We the examined the predicted profiles of all the active species and clustered them in four clusters according to their dynamic behaviour. Finally, we used parametric sensitivity analysis to show the role of negative feedback loops in controlling the robustness of the system. CONCLUSIONS: The presented ISP model is a powerful tool for data simulation and can be used in combination with experimental approaches to guide the experimental design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to Biomodels Database (https://www.ebi.ac.uk/biomodels-main/# MODEL 1604100005). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-016-0281-4) contains supplementary material, which is available to authorized users

Bilayer Microfluidic Device for Combinatorial Plug Production

Droplet microfluidics is a versatile tool that allows the execution of a large number of reactions in chemically distinct nanoliter compartments. Such systems have been used to encapsulate a variety of biochemical reactions - from incubation of single cells to implementation of PCR reactions, from genomics to chemical synthesis. Coupling the microfluidic channels with regulatory valves allows control over their opening and closing, thereby enabling the rapid production of large-scale combinatorial libraries consisting of a population of droplets with unique compositions. In this paper, protocols for the fabrication and operation of a pressure-driven, PDMS-based bilayer microfluidic device that can be utilized to generate combinatorial libraries of water-in-oil emulsions called plugs are presented. By incorporating software programs and microfluidic hardware, the flow of desired fluids in the device can be controlled and manipulated to generate combinatorial plug libraries and to control the composition and quantity of constituent plug populations. These protocols will expedite the process of generating combinatorial screens, particularly to study drug response in cells from cancer patient biopsies

Prediction of human population responses to toxic compounds by a collaborative competition

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000-Genomes Project. The challenge participants developed algorithms to predict inter-individual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against a blinded experimental dataset. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson’s r<0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r<0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal

A Boolean Approach to Linear Prediction for Signaling Network Modeling

The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) “Predictive signaling network modeling” challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single-stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases

Inference in systems biology: modelling approaches and applications

The main topic of this thesis is the study of biological regulatory systems using different computational modelling approaches in order to gain new insights into not yet completely understood biological processes. In "systems biology", mathematical models represent a powerful tool to study biological processes. Models are abstractions of reality always including some degree of simplification: an important ingredient of the modelling process, having a major role in suggesting the appropriate level of abstraction and simplification, is the purpose of the model, that is the question they have to answer. This thesis is focused on the analysis of how models of different complexity appropriately describe the available data to achieve a given purpose. Such analysis guides the choice of the most appropriate degree of simplification of the system under study that allows neglecting some aspects without compromising the results of the model. Three levels of detail for inference and modelling are analyzed in this thesis depending on the system under consideration. The first level is the network level, where molecules are nodes connected by edges and the interest is in the inference of the topology of connections at large scale. In the second level the network is interpreted as a mean to produce qualitative simulations and predictions which can be compared with experimental data. The third level of detail consist in a more mechanistic dynamic description of the system using ordinary differential equations but limiting the analysis to small subsystems. For each level of detail, appropriate approaches have been developed and applied to in silico and real data of different biological systems. Finally, different modelling appraches have been integrated to analyze insulin signalling pathway on different levels of simplification using a novel experimental dataset collected specifically for this purposeQuesta tesi di dottorato è incentrata sullo studio dei sistemi biologici mediante l'utilizzo di diversi approcci di modellistica computazionale, al fine di esplorare processi biologici non ancora chiari. Nella "systems biology", i modelli matematici rappresentano un potente mezzo per studiare i processi biologici. I modelli sono astrazioni della realtà e possono includere diversi livelli di semplificazione a seconda del loro scopo. La tesi è focalizzata sull'analisi di come, modelli di diversa complessità, possono essere utilizzati per raggiungere diversi scopi. Questa analisi guida la scelta del livello di semplificazione della realtà più adatto per trascurare certi dettagli senza però compromettere la sua applicazione. Tre livelli di dettaglio sono analizzati in questa tesi. Il primo livello è la rete, le molecole sono considerate come nodi connessi tra di loro da archi e si è interessati ad inferire la topologia delle connessioni su larga scala. Nel secondo livello, la rete è interpretata come un mezzo per produrre simulazioni qualitative che possono essere confrontate con i dati reali. Il terzo livello di dettaglio consiste in una descrizione dinamica meccanicistica del sistema, mediate l'utilizzo di equazioni differenziali ordinarie ma limitando l'analisi a sottosistemi di dimensioni ridotte. Per ogni livello di dettaglio, sono stati sviluppati approcci adeguati, poi applicati a dati in silico e a dati reali relativi a diversi sistemi biologici. Diversi approcci di modellistica sono stati integrati per l'analisi del pathway del signalling dell'insulina considerando diversi livelli di semplificazione e utilizzando un dataset sperimentale raccolto specificatamente per questo scop

Multi-omics profiling of the tumor microenvironment: Paving the way to precision immuno-oncology

The tumor microenvironment (TME) is a multifaceted ecosystem characterized by profound cellular heterogeneity, dynamicity, and complex intercellular cross-talk. The striking responses obtained with immune checkpoint blockers, i.e. antibodies targeting immune-cell regulators to boost antitumor immunity, have demonstrated the enormous potential of anticancer treatments that target TME components other than tumor cells. However, as checkpoint blockade is currently beneficial only to a limited fraction of patients, there is an urgent need to understand the mechanisms orchestrating the immune response in the TME to guide the rational design of more effective anticancer therapies. In this Mini Review, we give an overview of the methodologies that allow studying the heterogeneity of the TME from multi-omics data generated from bulk samples, single cells, or images of tumor-tissue slides. These include approaches for the characterization of the different cell phenotypes and for the reconstruction of their spatial organization and inter-cellular cross-talk. We discuss how this broader vision of the cellular heterogeneity and plasticity of tumors, which is emerging thanks to these methodologies, offers the opportunity to rationally design precision immuno-oncology treatments. These developments are fundamental to overcome the current limitations of targeted agents and checkpoint blockers and to bring long-term clinical benefits to a larger fraction of cancer patients

Integrating literature-constrained and data-driven inference of signalling networks

This is an Open Access article distributed under the terms of the Creative Commons Attribution License.-- et al.Recent developments in experimental methods allow generating increasingly larger signal transduction datasets. Two main approaches can be taken to derive from these data a mathematical model: to train a network (obtained e.g. from literature) to the data, or to infer the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, while literature- constrained methods cannot deal with incomplete networks. Results: We present an efficient approach, implemented in the R package CNORfeeder, to integrate literature-constrained and datadriven methods to infer signalling networks from perturbation experiments. Our method extends a given network with links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. We apply CNORfeeder to a network of growth and inflammatory signalling, obtaining a model with superior data fit in the human liver cancer HepG2 and proposes potential missing pathways.JSR thanks funding from EU-7FP-BioPreDyn, JdlR from EU FP7-HEALTH-2007-B (ref. 223411), Spanish ISCiii (ref. PS09/00843), and Junta Castilla y Leon (ref. CSI07A09). FE was partially supported by the “Borsa Gini” scholarship, awarded by “Fondazione Aldo Gini”, Padova, Italy.Peer reviewe