Modeling and Simulation of Biological Systems through Electronic Design Automation techniques

Modeling and simulation of biological systems is a key requirement for integrating invitro and in-vivo experimental data. In-silico simulation allows testing different experimental conditions, thus helping in the discovery of the dynamics that regulate the system. These dynamics include errors in the cellular information processing that are responsible for diseases such as cancer, autoimmunity, and diabetes as well as drug effects to the system (Gonalves, 2013). In this context, modeling approaches can be classified into two categories: quantitative and qualitative models. Quantitative modeling allows for a natural representation of molecular and gene networks and provides the most precise prediction. Nevertheless, the lack of kinetic data (and of quantitative data in general) hampers its use for many situations (Le Novere, 2015). In contrast, qualitative models simplify the biological reality and are often able to reproduce the system behavior. They cannot describe actual concentration levels nor realistic time scales. As a consequence, they cannot be used to explain and predict the outcome of biological experiments that yield quantitative data. However, given a biological network consisting of input (e.g., receptors), intermediate, and output (e.g., transcription factors) signals, they allow studying the input-output relationships through discrete simulation (Samaga, 2013). Boolean models are gaining an increasing interest in reproducing dynamic behaviors, understanding processes, and predicting emerging properties of cellular signaling networks through in-silico experiments. They are emerging as a valid alternative to the quantitative approaches (i.e., based on ordinary differential equations) for exploratory modeling when little is known about reaction kinetics or equilibrium constants in the context of gene expression or signaling. Even though several approaches and software have been recently proposed for logic modeling of biological systems, they are limited to specific contexts and they lack of automation in analyzing biological properties such as complex attractors, and molecule vulnerability. This thesis proposes a platform based on Electronic Design Automation (EDA) technologies for qualitative modeling and simulation of Biological Systems. It aims at overtaking limitations that affect the most recent qualitative tools

Affecting the macrophage response to infection by integrating signaling and gene-regulatory networks

Obesity has reached epidemic proportions in recent years. The World Health Organization estimated in 2008 that 1.4 billion people were overweight of whom 500 million were obese. Obesity associates with a wide range of conditions, such as cardiovascular diseases, cancer, diabetes, and neurological disorders, and causes approximately 2.8 million deaths each year. Many studies have established that obesity strongly impacts the normal function of the immune system: it dysregulates production of inflammatory and anti–inflammatory cytokines, alters numbers of immune cells, and causes an overall weaker immune response. Developing therapies that aim to improve the immune response is crucial in order to increase the quality of life of obese subjects and to reduce their ever–increasing healthcare-related costs. The long-term objective of this work is to contribute to the development of therapies that can increase the immune response in obese macrophages. In particular, gene modifications adjusting the response to infection in obese macrophages closer to that of lean macrophages are desired. To this end, the present work focused on the Toll-like Receptors (TLRs), which play an essential role in the detection of pathogens and the initiation of both innate and acquired immune responses. Genes essential to the transmission of the infection signal were first identified using a model of the TLR signaling pathways. These genes provided the basis for reconstructing a gene regulatory network that not only accounts for information coming from the TLRs, but also regulates key reactions within the pathways. The topology and regulatory functions of this network were identified by applying novel computational techniques to time-series gene-expression datasets. The TLR signaling and gene-regulatory networks were then integrated to develop a modeling framework for macrophage that predicts the time behavior of several markers for infection. Finally, formal verification techniques were used to demonstrate that the model satisfies several properties characteristic of the response to infection in macrophage. The work detailed in this dissertation offers a suitable platform for developing and testing biological hypotheses that aim to improve responses to infection

Using systems biology to investigate how age-related changes in TGFβ signalling alter pro-inflammatory stimuli

PhD ThesisOsteoarthritis (OA) is a degenerative condition caused by dysregulation of multiple molecular signalling pathways. This dysregulation results in damage to cartilage, a smooth and protective tissue that enables low friction articulation of synovial joints. Matrix metalloproteinases (MMPs), especially MMP13, are key enzymes in the cleavage of type II collagen which is a vital component for cartilage integrity. Various stimuli have been identified as inducers of MMP expression such as excessive load, injury and inflammation. Although previously considered a non-inflammatory arthritis, recent research has shown that inflammation may play an important role in OA development. A novel meta-analysis of microarray data from OA patients was used to create a cytoscape network representative of human OA. This enabled the identification of key processes in OA development, of which inflammation was prominent. Examining various different signalling pathways highlighted a role for transforming growth factor beta (TGFβ) in protecting against pro-inflammatory cytokine-mediated MMP expression. Indeed, TGFβ plays key roles in all facets of cartilage biology including development and maintenance of cartilage integrity. With age there is a change in the ratio of two TGFβ type I receptors (ALK1/ALK5), a shift that results in TGFβ losing its protective role in cartilage homeostasis. Instead, TGFβ promotes cartilage degradation and this correlates with the spontaneous development of OA in murine models. However, the mechanism by which TGFβ protects against pro-inflammatory responses and how this changes with age has not been extensively studied. Mathematical modelling has previously revealed how stochastic changes in TGFβ signalling during ageing led to the upregulation of MMPs. I have expanded the TGFβ section of this model to incorporate the pro-inflammatory stimulus interleukin-1 (IL-1) + oncostatin M (OSM) in order to investigate how TGFβ mediates MMP repression, specifically MMP-13. TGFβ signalling appears to interact with the activator protein 1 (AP-1) complex, which has an important role in MMP upregulation. However, the model indicates this interaction alone is insufficient to mediate the full effect of TGFβ, predicting it may also reduce MMP-13 mRNA stability. Furthermore, the model enabled me to predict how age alters these interactions; it suggested TGFβ would provide limited repression with a prolonged inflammatory response. Combining the modelled genes with the microarray network provided a global overview of how alterations in one pathway can affect others and lead to OA development. This study therefore demonstrates the power of combining computational biology with experimentally-derived data to provide insight into the importance of TGFβ signalling, and how age-related changes can lead to cartilage damage and OA development.Centre of Integrated Research into Musculoskeletal Ageing (CIMA), Arthritis research UK and the Medical Research Counci

Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics

Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics

Modeling and Analysis of Signal Transduction Networks

Biological pathways, such as signaling networks, are a key component of biological systems of each living cell. In fact, malfunctions of signaling pathways are linked to a number of diseases, and components of signaling pathways are used as potential drug targets. Elucidating the dynamic behavior of the components of pathways, and their interactions, is one of the key research areas of systems biology. Biological signaling networks are characterized by a large number of components and an even larger number of parameters describing the network. Furthermore, investigations of signaling networks are characterized by large uncertainties of the network as well as limited availability of data due to expensive and time-consuming experiments. As such, techniques derived from systems analysis, e.g., sensitivity analysis, experimental design, and parameter estimation, are important tools for elucidating the mechanisms involved in signaling networks. This Special Issue contains papers that investigate a variety of different signaling networks via established, as well as newly developed modeling and analysis techniques

From physics to pharmacology?

Over the last fifty years there has been an explosion of biological data, leading to the realization that to fully explain biological mechanisms it is necessary to interpret them as complex dynamical systems. The first stage of this interpretation is to determine which components (proteins, genes or metabolites) of the system interact. This is usually represented by a graph, or network. The behavior of this network can then be investigated using mathematical modeling. In vivo these biological networks show several remarkable (and seemingly paradoxical) properties including robustness, plasticity and sensitivity. Erroneous behavior of these networks is often associated with disease. Hence understanding the system-level properties can have important implications for the treatment of disease. Systems biology is an organized approach to quantitatively describe and elucidate the behavior of these complex networks. This review focuses on the progress and future challenges of a systems approach to biology

Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process