A Study of the PDGF Signaling Pathway with PRISM

In this paper, we apply the probabilistic model checker PRISM to the analysis of a biological system -- the Platelet-Derived Growth Factor (PDGF) signaling pathway, demonstrating in detail how this pathway can be analyzed in PRISM. We show that quantitative verification can yield a better understanding of the PDGF signaling pathway.Comment: In Proceedings CompMod 2011, arXiv:1109.104

A review of modelling and verification approaches for computational biology

This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties

Computational approaches for translational oncology: Concepts and patents

Background: Cancer is a heterogeneous disease, which is based on an intricate network of processes at different spatiotemporal scales, from the genome to the tissue level. Hence the necessity for the biomedical and pharmaceutical research to work in a multiscale fashion. In this respect, a significant help derives from the collaboration with theoretical sciences. Mathematical models can in fact provide insights into tumor-related processes and support clinical oncologists in the design of treatment regime, dosage, schedule and toxicity. Objective and Method: The main objective of this article is to review the recent computational-based patents which tackle some relevant aspects of tumor treatment. We first analyze a series of patents concerning the purposing the purposing or repurposing of anti-tumor compounds. These approaches rely on pharmacokinetics and pharmacodynamics modules, that incorporate data obtained in the different phases of clinical trials. Similar methods are also at the basis of other patents included in this paper, which deal with treatment optimization, in terms of maximizing therapy efficacy while minimizing side effects on the host. A group of patents predicting drug response and tumor evolution by the use of kinetics graphs are commented as well. We finally focus on patents that implement informatics tools to map and screen biological, medical, and pharmaceutical knowledge. Results and Conclusions: Despite promising aspects (and an increasing amount of the relative literature), we found few computational-based patents: There is still a significant effort to do for allowing modelling approaches to become an integral component of the pharmaceutical research

BAYESIAN INTEGRATIVE ANALYSIS OF OMICS DATA

Technological innovations have produced large multi-modal datasets that range in multiplatform genomic data, pathway data, proteomic data, imaging data and clinical data. Integrative analysis of such data sets have potentiality in revealing important biological and clinical insights into complex diseases like cancer. This dissertation focuses on Bayesian methodology establishment in integrative analysis of radiogenomics and pathway driver detection applied in cancer applications. We initially present Radio-iBAG that utilizes Bayesian approaches in analyzing radiological imaging and multi-platform genomic data, which we establish a multi-scale Bayesian hierarchical model that simultaneously identifies genomic and radiomic, i.e., radiology-based imaging markers, along with the latent associations between these two modalities, and to detect the overall prognostic relevance of the combined markers. Our method is motivated by and applied to The Cancer Genome Atlas glioblastoma multiforme data set, wherein it identifies important magnetic resonance imaging features and the associated genomic platforms that are also significantly related with patient survival times. For another aspect of integrative analysis, we then present pathDrive that aims to detect key genetic and epigenetic upstream drivers that influence pathway activity. The method is applied into colorectal cancer incorporated with its four molecular subtypes. For each of the pathways that significantly differentiates subgroups, we detect important genomic drivers that can be viewed as “switches” for the pathway activity. To extend the analysis, finally, we develop proteomic based pathway driver analysis for multiple cancer types wherein we simultaneously detect genomic upstream factors that influence a specific pathway for each cancer type within the cancer group. With Bayesian hierarchical model, we detect signals borrowing strength from common cancer type to rare cancer type, and simultaneously estimate their selection similarity. Through simulation study, our method is demonstrated in providing many advantages, including increased power and lower false discovery rates. We then apply the method into the analysis of multiple cancer groups, wherein we detect key genomic upstream drivers with proper biological interpretation. The overall framework and methodologies established in this dissertation illustrate further investigation in the field of integrative analysis of omics data, provide more comprehensive insight into biological mechanisms and processes, cancer development and progression

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

Identification of idh1 mutation-related gene signature of glioblastoma multiforme

Background: Glioblastoma multiforme (GBM) is a type of commonly occurred malignant astrocytoma with an extremely poor prognosis. GBMs display a remarkable genetic variability, and it is essential to study the genomic alterations and pathway dysregulations based on the different tumor entities. The gene IDH1 encodes cytosolic isocitrate dehydrogenase 1, which catalyzes the reactions of oxidative decarboxylation of isocitrate to Alpha-ketoglutarate. Different types of mutation of IDH1 has been found in gliomas and GBMs, especially in secondary GBMs. Among the IDH1 mutations, R132H mutation is the most prominent one. IDH1 mutation in GBMs is correlated with a longer survival time, and no IDH1 mutations are reported in many other tumor types. Thus IDH1 is hypothesized as crucial in the pathogenesis of GBMs, and it is regarded as a potential drug target. The fundamental goal of this study is to identify a gene signature correlated with IDH1 mutation in GBMs. And related genes and biological pathways are also studied. Methods: Most of the work of data collection and analysis are achieved with R packages. The step-down maxT method is adopted to perform the multiple testing procedure in order to find differently expressed genes. The p-values of statistical tests are corrected by controlling FWER. The clustering result is explicated as heatmap, and clinical data is elucidated with boxplot and Kaplan Meier-plot. Analysis of GO and KEGG pathways are used to extract more information from the genes. And the results are visualized as graphs in Cytoscape. Results: A framework is created for identifying gene signatures as well as studying biological pathways. The expression data from 548 samples are collected, and 58 genes out of 12042 genes are identified as differently expressed. Finally a gene signature with 50 genes are proposed. Conclusion: Microarray technology and statistics methods are effective for the studying of alterations in gene expression and biological pathways. The gene signature proposed by this study can distinguish samples harboring IDH1 mutation from GBMs. And future researches are necessary to corroborate and extend the results

Computational Proteomics Using Network-Based Strategies

This thesis examines the productive application of networks towards proteomics, with a specific biological focus on liver cancer. Contempory proteomics (shot- gun) is plagued by coverage and consistency issues. These can be resolved via network-based approaches. The application of 3 classes of network-based approaches are examined: A traditional cluster based approach termed Proteomics Expansion Pipeline), a generalization of PEP termed Maxlink and a feature-based approach termed Proteomics Signature Profiling. PEP is an improvement on prevailing cluster-based approaches. It uses a state- of-the-art cluster identification algorithm as well as network-cleaning approaches to identify the critical network regions indicated by the liver cancer data set. The top PARP1 associated-cluster was identified and independently validated. Maxlink allows identification of undetected proteins based on the number of links to identified differential proteins. It is more sensitive than PEP due to more relaxed requirements. Here, the novel roles of ARRB1/2 and ACTB are identified and discussed in the context of liver cancer. Both PEP and Maxlink are unable to deal with consistency issues, PSP is the first method able to deal with both, and is termed feature-based since the network- based clusters it uses are predicted independently of the data. It is also capable of using real complexes or predicted pathway subnets. By combining pathways and complexes, a novel basis of liver cancer progression implicating nucleotide pool imbalance aggravated by mutations of key DNA repair complexes was identified. Finally, comparative evaluations suggested that pure network-based methods are vastly outperformed by feature-based network methods utilizing real complexes. This is indicative that the quality of current networks are insufficient to provide strong biological rigor for data analysis, and should be carefully evaluated before further validations.Open Acces

Discovering meaning from biological sequences: focus on predicting misannotated proteins, binding patterns, and G4-quadruplex secondary

Proteins are the principal catalytic agents, structural elements, signal transmitters, transporters, and molecular machines in cells. Experimental determination of protein function is expensive in time and resources compared to computational methods. Hence, assigning proteins function, predicting protein binding patterns, and understanding protein regulation are important problems in functional genomics and key challenges in bioinformatics. This dissertation comprises of three studies. In the first two papers, we apply machine-learning methods to (1) identify misannotated sequences and (2) predict the binding patterns of proteins. The third paper is (3) a genome-wide analysis of G4-quadruplex sequences in the maize genome. The first two papers are based on two-stage classification methods. The first stage uses machine-learning approaches that combine composition-based and sequence-based features. We use either a decision trees (HDTree) or support vector machines (SVM) as second-stage classifiers and show that classification performance reaches or outperforms more computationally expensive approaches. For study (1) our method identified potential misannotated sequences within a well-characterized set of proteins in a popular bioinformatics database. We identified misannotated proteins and show the proteins have contradicting AmiGO and UniProt annotations. For study (2), we developed a three-phase approach: Phase I classifies whether a protein binds with another protein. Phase II determines whether a protein-binding protein is a hub. Phase III classifies hub proteins based on the number of binding sites and the number of concurrent binding partners. For study (3), we carried out a computational genome-wide screen to identify non-telomeric G4-quadruplex (G4Q) elements in maize to explore their potential role in gene regulation for flowering plants. Analysis of G4Q-containing genes uncovered a striking tendency for their enrichment in genes of networks and pathways associated with electron transport, sugar degradation, and hypoxia responsiveness. The maize G4Q elements may play a previously unrecognized role in coordinating global regulation of gene expression in response to hypoxia to control carbohydrate metabolism for anaerobic metabolism. We demonstrated that our three studies have the ability to predict and provide new insights in classifying misannotated proteins, understanding protein binding patterns, and identifying a potentially new model for gene regulation

Macrophage mechanosensing of the tissue environment and signal integration through the cytoskeleton

The extracellular matrix (ECM) is an organized assembly of proteins and polysaccharides that is produced by cells and forms the physical environment in which cells reside. Together, diverse cell types and their surrounding extracellular matrix form units of organization known as tissues, which make up organs. The ECM gives rise to the particular architecture and mechanical properties of each organ. During tissue repair, cells known as myofibroblasts deposit large quantities of ECM, in order to reconstruct the injured tissue. In normal tissue repair, that reparative phase is followed by a resolution phase, in which cells such as macrophages degrade and remodel excess extracellular matrix, returning the tissue to a homeostatic state. In fibrotic diseases, however, tissue repair persists, leading to the progressive accumulation of dense, stiff extracellular matrix that prevents normal organ function and leads to organ failure. Macrophages are immune cells that reside within all tissues and have important non-immunologic functions, including sensing and regulating features of the tissue environment. They often act as sensors within a homeostatic circuit, monitoring a variable of interest and communicating with other cells, known as effectors, that can correct the variable when it deviates from the desired range. During tissue repair, monocytes from the blood enter the tissue and differentiate into macrophages, where they play a critical role both in driving fibroblast ECM production and in resolving tissue repair through ECM degradation. We hypothesized that macrophages act as sensors of the extracellular matrix within tissues, both to maintain ECM homeostasis under normal conditions and to monitor the progression of tissue repair to ensure an appropriate transition to resolution and avoid fibrosis. In the studies presented in this dissertation, using in vitro hydrogel systems to mimic essential elements of tissue biology, we find that macrophages can sense changes in the extracellular matrix and that they respond by regulating a specific subset of their gene expression program involved in tissue repair. This program includes the protein FIZZ1, which drives fibroblast ECM deposition and, we find, is suppressed by increased ECM, suggesting that macrophages may be involved in a negative feedback loop to control tissue repair. We determine that macrophages sense, in particular, the mechanical properties of the extracellular matrix, and that they employ a novel, integrin-independent mechanosensing mechanism. Macrophage mechanosensing is mediated by intracellular changes in the dynamics of the actin cytoskeleton, which ultimately control chromatin availability and binding of the transcription factor C/EBP to specific genomic targets. Furthermore, we identify that the macrophage growth factor, macrophage colony stimulating factor (MCSF), converges on these cytoskeletal dynamics and downstream regulatory mechanisms to control the same gene expression program. Thus, we find that macrophages integrate mechanical and biochemical information about the tissue environment through changes in their actin cytoskeleton, in order to regulate their tissue repair program. In the final chapters, we present some of the implications of these findings, as well as broader perspectives on tissue biology, homeostasis, and inflammation