A core calculus for the analysis and implementation of biologically inspired languages

The application of Concurrency Theory to Systems Biology is in its earliest stage of progress. The metaphor of cells as computing systems by Regev and Shapiro opened the employment of concurrent languages for the modelling of biological systems. Their peculiar characteristics led to the design of many bio-inspired formalisms which achieve higher faithfulness and specificity. In this thesis we present pi@, an extremely simple and conservative extension of the pi-calculus representing a keystone in this respect, thanks to its expressiveness capabilities. The pi@ calculus is obtained by the addition of polyadic synchronisation and priority to the pi-calculus, in order to achieve compartment semantics and atomicity of complex operations respectively. In its direct application to biological modelling, the stochastic variant of the calculus, Spi@, is shown able to model consistently several phenomena such as formation of molecular complexes, hierarchical subdivision of the system into compartments, inter-compartment reactions, dynamic reorganisation of compartment structure consistent with volume variation. The pivotal role of pi@ is evidenced by its capability of encoding in a compositional way several bio-inspired formalisms, so that it represents the optimal core of a framework for the analysis and implementation of bio-inspired languages. In this respect, the encodings of BioAmbients, Brane Calculi and a variant of P Systems in pi@ are formalised. The conciseness of their translation in pi@ allows their indirect comparison by means of their encodings. Furthermore it provides a ready-to-run implementation of minimal effort whose correctness is granted by the correctness of the respective encoding functions. Further important results of general validity are stated on the expressive power of priority. Several impossibility results are described, which clearly state the superior expressiveness of prioritised languages and the problems arising in the attempt of providing their parallel implementation. To this aim, a new setting in distributed computing (the last man standing problem) is singled out and exploited to prove the impossibility of providing a purely parallel implementation of priority by means of point-to-point or broadcast communication.

Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Studio del network metabolico umano: modelli e applicazioni.

Il lavoro di tesi si basa sull'analisi del database pubblico ReconX, il quale costituisce lo stato dell'arte della ricostruzione in silico del metabolismo umano. Il modello che se ne può estrarre è stato impiegato per simulazioni di metabolismo cellulare in condizioni di ossigeno variabile, valutando l'impatto della carenza di ossigeno sulle reazioni e su i pathways metabolici. Le tecniche impiegate appartengono alla systems biology e sono di tipo bioinformatico e riguardano flux balance analysis e flux variability analysis. I risultati trovati vengono quindi confrontati con la letteratura di settore. E' stato inoltre possibile estrarre dei sotto network dal modello principale, analizzando la rete di connessioni esistente fra i metaboliti e fra le reazioni separatamente. Viene estratto e studiato anche il network di interazione fra pathways, su cui è introdotta una misura ad hoc per valutare la forza di connessione fra i vari processi. Su quest'ultimo network viene anche effettuata un'analisi di tipo stocastico, mostrando che tecniche di tipo markoviano possono essere applicate per indagini diverse da quelle più in uso basate sui flussi. Infine viene mostrata una possibile soluzione per visualizzare graficamente i network metabolici così costituiti.

Crosstalk between B lymphocytes, microbiota and the intestinal epithelium governs immunity versus metabolism in the gut

Using a systems biology approach, we discovered and dissected a three-way interaction between the immune system, the intestinal epithelium and the microbiota. We found that, in the absence of B cells, or of IgA, and in the presence of the microbiota, the intestinal epithelium launches its own protective mechanisms, upregulating interferon-inducible immune response pathways and simultaneously repressing Gata4-related metabolic functions. This shift in intestinal function leads to lipid malabsorption and decreased deposition of body fat. Network analysis revealed the presence of two interconnected epithelial-cell gene networks, one governing lipid metabolism and another regulating immunity, that were inversely expressed. Gene expression patterns in gut biopsies from individuals with common variable immunodeficiency or with HIV infection and intestinal malabsorption were very similar to those of the B cell-deficient mice, providing a possible explanation for a longstanding enigmatic association between immunodeficiency and defective lipid absorption in humans.

Assessing The Role Of Multi-protein Complexes In Determining Phenotype

Understanding regulatory mechanisms in complex biological systems is an important challenge, in particular to understand disease mechanisms, and to discover new therapies and drugs. In this paper, we consider the important question of cellular regulation of phenotype. Using single gene deletion data, we address the problem of linking a phenotype to underlying functional roles in the organism and provide a sound computational and statistical paradigm that can be extended to address more complex experimental settings such as multiple deletions. We apply the proposed approaches to publicly available data sets to demonstrate strong evidence for the involvement of multi-protein complexes in the phenotypes studied.

Enumerating constrained elementary flux vectors of metabolic networks

The concept of elementary vector is generalised to the case where the steady-state space of the metabolic network is not a flux cone but is a general polyhedron due to further inhomogeneous constraints on the flows through some of the reactions. On one hand, this allows to selectively enumerate elementary modes which satisfy certain optimality criteria and this can yield a large computational gain compared with full enumeration. On the other hand, in contrast to the single optimum found by executing a linear program, this enables a comprehensive description of the set of alternate optima often encountered in flux balance analysis. The concepts are illustrated on a metabolic network model of human cardiac mitochondria.

Has "Omnics" Gone Viral?

Discussion on viruses from mild to wild and how using "omnics" (such as genomes, mRNA, proteins, metabolites) can help uncover unexpected biology.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

Understanding acquired resistance to Lapatinib in breast cancer cells

Signaling through epidermal growth factor receptor (EGFR/ErbB) family members plays a very important role in regulating proliferation, development, and malignant transformation of mammary epithelial cells. ErbB family members are often over-expressed in human breast carcinomas. Lapatinib is an ErbB1 and ErbB2 tyrosine kinase inhibitor that has been shown to have anti-proliferative effects in breast and lung cancer cells. Cells treated with Lapatinib undergo G1 phase arrest, followed by apoptosis. Lapatinib has been approved for clinical use, though patients have developed resistance to the drug, as seen previously with other EGFR inhibitors. Moreover, the therapeutic efficacy varies significantly within the patient population, and the mechanism of drug sensitivity is not fully understood. Expression levels of ErbB2 are used as a prognostic marker for Lapatinib response; however, even among breast tumor cell lines that express similar levels of ErbB2 there is marked difference in their proliferative responses to Lapatinib. To understand the mechanisms of acquired resistance, we established a cell line SkBr3-R that is resistant to Lapatinib, from a Lapatinib-sensitive breast tumor cell line, SkBr3. We have characterized the cell lines and demonstrated that Lapatinib resistance in our system is not facilitated by receptor-level activity or by previously known mutations in the ErbB receptors. Significant changes were observed in cell proliferation, cell migration, cell cycle and cell death between the Lapatinib resistant SkBr3-R and sensitive SkBr3 cell lines. Recent studies have suggested STAT3 is upregulated in Lapatinib resistant tumors in association with ErbB signaling. We investigated the role that STAT3 may play in Lapatinib resistance and discovered higher STAT3 activity in these resistant cells. In addition, transcriptional profiling indicated higher expression of STAT3 target genes, as well as of other genes that promote survival. The gene array data also revealed cell cycle regulators and cell adhesion/junction component genes as possible mediator of Lapatinib resistance. Altogether, this study has identified several possible mechanisms of Lapatinib resistance.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

MODELLING β2AR REGULATION

The β2 adrenergic receptor (β2AR) regulates smooth muscle relaxation in the vasculature and airways. Long- and Short-acting β-agonists (LABAs/SABAs) are widely used in treatment of chronic obstructive pulmonary disorder (COPD) and asthma. Despite their widespread clinical use we do not understand well the dominant β2AR regulatory pathways that are stimulated during therapy and bring about tachyphylaxis, which is the loss of drug effects. Thus, an understanding of how the β2AR responds to various β-agonists is crucial to their rational use. Towards that end we have developed deterministic models that explore the mechanism of drug- induced β2AR regulation. These mathematical models can be classified into three classes; (i) Six quantitative models of SABA-induced G protein coupled receptor kinase (GRK)-mediated β2AR regulation; (ii) Three phenomenological models of salmeterol (a LABA)-induced GRK-mediated β2AR regulation; and (iii) One semi-quantitative, unified model of SABA-induced GRK-, protein kinase A (PKA)-, and phosphodiesterase (PDE)-mediated regulation of β2AR signalling. The various models were constrained with all or some of the following experimental data; (i) GRK-mediated β2AR phosphorylation in response to various LABAs/SABAs; (ii) dephosphorylation of the GRK site on the β2AR; (iii) β2AR internalisation; (iv) β2AR recycling; (v) β2AR desensitisation; (vi) β2AR resensitisation; (vii) PKA-mediated β2AR phosphorylation in response to a SABA; and (viii) LABA/SABA induced cAMP profile ± PDE inhibitors. The models of GRK-mediated β2AR regulation show that plasma membrane dephosphorylation and recycling of the phosphorylated β2AR are required to reconcile with the measured dephosphorylation kinetics. We further used a consensus model to predict the consequences of rapid pulsatile agonist stimulation and found that although resensitisation was rapid, the β2AR system retained the memory of prior stimuli and desensitised much more rapidly and strongly in response to subsequent stimuli. This could explain tachyphylaxis of SABAs over repeated use in rescue therapy of asthma patients. The LABA models show that the long action of salmeterol can be explained due to decreased stability of the arrestin/β2AR/salmeterol complex. This could explain long action of β-agonists used in maintenance therapy of asthma patients. Our consensus model of PKA/PDE/GRK-mediated β2AR regulation is being used to identify the dominant β2AR desensitisation pathways under different therapeutic regimens in human airway cells. In summary our models represent a significant advance towards understanding agonist-specific β2AR regulation that will aid in a more rational use of the β2AR agonists in the treatment of asthma.

The human IgG anti-carbohydrate repertoire exhibits a universal architecture and contains specificity for microbial attachment sites.

Despite the paradigm that carbohydrates are T cell-independent antigens, isotype-switched glycan-specific immunoglobulin G (IgG) antibodies and polysaccharide-specific T cells are found in humans. We used a systems-level approach combined with glycan array technology to decipher the repertoire of carbohydrate-specific IgG antibodies in intravenous and subcutaneous immunoglobulin preparations. A strikingly universal architecture of this repertoire with modular organization among different donor populations revealed an association between immunogenicity or tolerance and particular structural features of glycans. Antibodies were identified with specificity not only for microbial antigens but also for a broad spectrum of host glycans that serve as attachment sites for viral and bacterial pathogens and/or exotoxins. Tumor-associated carbohydrate antigens were differentially detected by IgG antibodies, whereas non-IgG2 reactivity was predominantly absent. Our study highlights the power of systems biology approaches to analyze immune responses and reveals potential glycan antigen determinants that are relevant to vaccine design, diagnostic assays, and antibody-based therapies.

Profiling invasiveness in head and neck cancer: recent contributions of genomic and transcriptomic approaches.

High-throughput molecular profiling approaches have emerged as precious research tools in the field of head and neck translational oncology. Such approaches have identified and/or confirmed the role of several genes or pathways in the acquisition/maintenance of an invasive phenotype and the execution of cellular programs related to cell invasion. Recently published new-generation sequencing studies in head and neck squamous cell carcinoma (HNSCC) have unveiled prominent roles in carcinogenesis and cell invasion of mutations involving NOTCH1 and PI3K-patwhay components. Gene-expression profiling studies combined with systems biology approaches have allowed identifying and gaining further mechanistic understanding into pathways commonly enriched in invasive HNSCC. These pathways include antigen-presenting and leucocyte adhesion molecules, as well as genes involved in cell-extracellular matrix interactions. Here we review the major insights into invasiveness in head and neck cancer provided by high-throughput molecular profiling approaches.

Clinical and inflammatory characteristics of the European U-BIOPRED adult severe asthma cohort

U-BIOPRED is a European Union consortium of 20 academic institutions, 11 pharmaceutical companies and six patient organisations with the objective of improving the understanding of asthma disease mechanisms using a systems biology approach.This cross-sectional assessment of adults with severe asthma, mild/moderate asthma and healthy controls from 11 European countries consisted of analyses of patient-reported outcomes, lung function, blood and airway inflammatory measurements.Patients with severe asthma (nonsmokers, n=311; smokers/ex-smokers, n=110) had more symptoms and exacerbations compared to patients with mild/moderate disease (n=88) (2.5 exacerbations versus 0.4 in the preceding 12 months; p<0.001), with worse quality of life, and higher levels of anxiety and depression. They also had a higher incidence of nasal polyps and gastro-oesophageal reflux with lower lung function. Sputum eosinophil count was higher in severe asthma compared to mild/moderate asthma (median count 2.99% versus 1.05%; p=0.004) despite treatment with higher doses of inhaled and/or oral corticosteroids.Consistent with other severe asthma cohorts, U-BIOPRED is characterised by poor symptom control, increased comorbidity and airway inflammation, despite high levels of treatment. It is well suited to identify asthma phenotypes using the array of "omic" datasets that are at the core of this systems medicine approach.