Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Dynamical Models of biological networks

In der Molekularbiologie sind mathematische Modelle von regulatorischen und metabolischen Netzwerken essentiell, um von einer Betrachtung isolierter Komponenten und Interaktionen zu einer systemischen Betrachtungsweise zu kommen. Genregulatorische Systeme eignen sich besonders gut zur Modellierung, da sie experimentell leicht zugänglich und manipulierbar sind. In dieser Arbeit werden verschiedene genregulatorische Netzwerke unter Zuhilfenahme von mathematischen Modellen analysiert. Weiteres wird ein Modell einer in silico Zelle vorgestellt und diskutiert. Zunächst werden zwei zyklische genregulatorische Netzwerke - der klassische Repressilator und ein Repressilator mit zusätzlicher Autoaktivierung – im Detail mit analytischen Methoden untersucht. Um den Einfluß zufällig schwankender Molekülzahlen auf die Dynamik der beiden Systeme zu untersuchen, werden stochastische Modelle erstellt und die beiden oszillierenden Systeme verglichen. Weiteres werden mögliche Auswirkungen von Genduplikationen auf ein einfaches genregulatorisches Netzwerk untersucht. Dazu wird zunächst ein kleines Netzwerk von GATA Transkriptionsfaktoren, das eine zentrale Rolle in der Regulation des Stickstoffmetabolismus in Hefe spielt, modelliert und das Modell mit experimentellen Daten verglichen, um Parameterregionen einschränken zu können. Außerdem werden potentielle Topologien genregulatorischer Netzwerke von GATA Transkriptionsfaktoren in verwandten Fungi mittels sequenzbasierender Methoden gesucht und verglichen. Im letzten Teil der Arbeit wird MiniCellSim vorgestellt, ein Modell einer selbständigen in silico Zelle. Es erlaubt ein dynamisches System, das eine Protozelle mit einem genregulatorischen Netzwerk, einem einfachen Metabolismus und einer Zellmembran beschreibt, aus einer Sequenz abzuleiten. Nachdem alle Parameter, die zur Berechnung des dynamischen Systems benötigt werden, ohne zusätzliche Eingabe nur aus der Sequenzinformation abgeleitet werden, kann das Modell für Studien zur Evolution von genregulatorischen Netzwerken verwendet werden.In this thesis different types of gene regulatory networks are analysed using mathematical models. Further a computational framework of a novel, self-contained in silico cell model is described and discussed. At first the behaviour of two cyclic gene regulatory systems - the classical repressilator and a repressilator with additional auto-activation - are inspected in detail using analytical bifurcation analysis. To examine the behaviour under random fluctuations, stochastic versions of the systems are created. Using the analytical results sustained oscillations in the stochastic versions are obtained, and the two oscillating systems compared. In the second part of the thesis possible implications of gene duplication on a simple gene regulatory system are inspected. A model of a small network formed by GATA-type transcription factors, central in nitrogen catabolite repression in yeast, is created and validated against experimental data to obtain approximate parameter values. Further, topologies of potential gene regulatory networks and modules consisting of GATA-type transcription factors in other fungi are derived using sequence-based approaches and compared. The last part describes MiniCellSim, a model of a self-contained in silico cell. In this framework a dynamical system describing a protocell with a gene regulatory network, a simple metabolism, and a cell membrane is derived from a string representing a genome. All the relevant parameters required to compute the time evolution of the dynamical system are calculated from within the model, allowing the system to be used in studies of evolution of gene regulatory and metabolic networks

In-silico-Systemanalyse von Biopathways

Chen M. In silico systems analysis of biopathways. Bielefeld (Germany): Bielefeld University; 2004.In the past decade with the advent of high-throughput technologies, biology has migrated from a descriptive science to a predictive one. A vast amount of information on the metabolism have been produced; a number of specific genetic/metabolic databases and computational systems have been developed, which makes it possible for biologists to perform in silico analysis of metabolism. With experimental data from laboratory, biologists wish to systematically conduct their analysis with an easy-to-use computational system. One major task is to implement molecular information systems that will allow to integrate different molecular database systems, and to design analysis tools (e.g. simulators of complex metabolic reactions). Three key problems are involved: 1) Modeling and simulation of biological processes; 2) Reconstruction of metabolic pathways, leading to predictions about the integrated function of the network; and 3) Comparison of metabolism, providing an important way to reveal the functional relationship between a set of metabolic pathways. This dissertation addresses these problems of in silico systems analysis of biopathways. We developed a software system to integrate the access to different databases, and exploited the Petri net methodology to model and simulate metabolic networks in cells. It develops a computer modeling and simulation technique based on Petri net methodology; investigates metabolic networks at a system level; proposes a markup language for biological data interchange among diverse biological simulators and Petri net tools; establishes a web-based information retrieval system for metabolic pathway prediction; presents an algorithm for metabolic pathway alignment; recommends a nomenclature of cellular signal transduction; and attempts to standardize the representation of biological pathways. Hybrid Petri net methodology is exploited to model metabolic networks. Kinetic modeling strategy and Petri net modeling algorithm are applied to perform the processes of elements functioning and model analysis. The proposed methodology can be used for all other metabolic networks or the virtual cell metabolism. Moreover, perspectives of Petri net modeling and simulation of metabolic networks are outlined. A proposal for the Biology Petri Net Markup Language (BioPNML) is presented. The concepts and terminology of the interchange format, as well as its syntax (which is based on XML) are introduced. BioPNML is designed to provide a starting point for the development of a standard interchange format for Bioinformatics and Petri nets. The language makes it possible to exchange biology Petri net diagrams between all supported hardware platforms and versions. It is also designed to associate Petri net models and other known metabolic simulators. A web-based metabolic information retrieval system, PathAligner, is developed in order to predict metabolic pathways from rudimentary elements of pathways. It extracts metabolic information from biological databases via the Internet, and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites, etc. The system also provides a navigation platform to investigate metabolic related information, and transforms the output data into XML files for further modeling and simulation of the reconstructed pathway. An alignment algorithm to compare the similarity between metabolic pathways is presented. A new definition of the metabolic pathway is proposed. The pathway defined as a linear event sequence is practical for our alignment algorithm. The algorithm is based on strip scoring the similarity of 4-hierarchical EC numbers involved in the pathways. The algorithm described has been implemented and is in current use in the context of the PathAligner system. Furthermore, new methods for the classification and nomenclature of cellular signal transductions are recommended. For each type of characterized signal transduction, a unique ST number is provided. The Signal Transduction Classification Database (STCDB), based on the proposed classification and nomenclature, has been established. By merging the ST numbers with EC numbers, alignments of biopathways are possible. Finally, a detailed model of urea cycle that includes gene regulatory networks, metabolic pathways and signal transduction is demonstrated by using our approaches. A system biological interpretation of the observed behavior of the urea cycle and its related transcriptomics information is proposed to provide new insights for metabolic engineering and medical care

Glucocorticoid-dependent regulation of molecular clocks and dendritic spines in the ventromedial prefrontal cortex

2022 Summer.Includes bibliographical references.Biological rhythms in the brain and periphery are governed by the suprachiasmatic nucleus of the hypothalamus (SCN) and the SCN's control of rhythmic adrenal glucocorticoid (GC) secretions via the hypothalamic pituitary adrenal (HPA) axis. The daily surge of GC secretions aid in the entrainment of molecular clocks throughout the body which physically, mentally, and metabolically prime an organism to function in accordance with the external light:dark (L:D) cycle. When key events of biological rhythms fail to match up with the external L:D cycle, various pathologies arise in the brain and periphery. To better understand the neural basis of pathologies caused by the disruption of biological rhythms, further investigation of key limbic regulatory brain regions is required. Thus, the studies described in this dissertation examine how biological rhythms in the ventromedial prefrontal cortex (vmPFC) are regulated by GC secretions. The vmPFC regulates fear acquisition, fear extinction, mood, and HPA axis function. Multiple brain regions exhibit time-of-day dependent variations in learning, long term potentiation (LTP), and dendritic morphology. GCs have been implicated in the regulation of dendritic structure in the context of stress. GCs are also known to regulate molecular clock entrainment via upregulation of Per1 transcription in a variety of tissues. In the present study, C57BL/6N mice were sacrificed at 3 distinct times of day (ZT3, ZT12, and ZT16, lights off at ZT12) and Per1 mRNA expression was measured in the infralimbic and prelimbic vmPFC subregions using droplet digital (dd)PCR after recovering from adrenalectomy or sham surgery for 10 days. Sham mice showed Per1 rhythmicity in both IL and PL, with peak expression occurring at ZT12. Adrenalectomized mice showed reductions in Per1 amplitude at ZT12 in both IL and PL, suggesting that the vmPFC molecular clock is entrained by diurnal GC oscillations. Thy1-eGFP mice were used to visualize and quantify dendritic spine density on layer V pyramidal dendrites at ZT 3, 12, and 16. Spine density in both PL and IL exhibited changes between the light (inactive) and dark (active) phases, with peak spine density observed at ZT16 and trough spine density observed at ZT3. These changes in spine density were restricted to changes in long thin and stubby type spines. To determine if changes in spine density is regulated by diurnal GC oscillations, the 11β-hydroxylase inhibitor metyrapone was administered 2 hours prior to the onset of the active phase (ZT10) daily for 7 days. Metyrapone administration blocked both the diurnal peak of plasma corticosterone and peak spine densities in the IL and PL at ZT16. These results suggest that vmPFC molecular clock gene and dendritic spine diurnal rhythms depend on intact diurnal GC oscillations. These findings establish a link between diurnal GC oscillations, the molecular clock, and synaptic plasticity. Additionally, these findings describe how the vmPFC changes across 24-hour periods, which provides a foundation for further investigation into how biological rhythms in the vmPFC may be altered in the context of circadian disruption, and how specific disease states may arise as a result

Computational Modeling of Inflammatory Mediators in Acute Illness: From Networks to Mechanisms

The acute inflammatory response is a complex defense mechanism that has evolved to respond rapidly to injury, infection, and other disruptions in homeostasis. The complex role of inflammation in health and disease has made it difficult to understand comprehensively. With the advent of high throughput technologies and the growth of systems biology, there has been an unprecedented amount of data and –omics analysis aimed at uncovering this complexity. However, there still remains a shortage of translational insights for acute inflammatory diseases from these studies. In this dissertation, we employ a comprehensive systems approach in order to study the coordination of inflammation and identify key control mechanisms, and how these map onto clinical outcomes. This process begins with collection of high-dimensional time course data of inflammatory mediators, followed by data-driven modeling and network inference that finally informs mechanistic computational models for prediction and analysis. In patients with pediatric acute liver failure (PALF), we inferred inflammatory networks and identified key differences between patients that were survivors versus non-survivors when other analyses proved inconclusive. We showed that inflammatory networks can be used both as biomarkers and to generate mechanistic hypotheses for this poorly understood disease. In experimental models of trauma as well as in human trauma patients, we identify a conserved central network motif of cross-regulating chemokines. We develop a logical model based on this hypothesized network, which is able to capture both inflammatory trajectory and clinical outcome differences among patients with differing injury severity. These studies suggest that the hypothesized cross-regulatory interactions among chemokines MIG, IP-10 and MCP-1 represents an important point of control regulating the progression of acute inflammation. We propose that further analysis and validation of this hypothesis will require targeted perturbation studies in cells and animals with iterative rounds of mechanistic model refinement. We explore an example of such a study focused on the anti-inflammatory effects of NAD+, wherein we characterize a signaling pathway that gives rise to a complex dose and time dependent induction of TGF-β1

Combining Network Modeling and Experimental Approaches to Predict Drug Combination Responses

Cancer is a lethal disease and complex at multiple levels of cell biology. Despite many advances in treatments, many patients do not respond to therapy. This is owing to the complexity of cancer-genetic variability due to mutations, the multi-variate biochemical networks within which drug targets reside and existence and plasticity of multiple cell states. It is generally understood that a combination of drugs is a way to address the multi-faceted drivers of cancer and drug resistance. However, the sheer number of testable combinations and challenges in matching patients to appropriate combination treatments are major issues. Here, we first present a general method of network inference which can be applied to infer biological networks. We apply this method to infer different kinds of networks in biological levels where cancer complexity resides-a biochemical network, gene expression and cell state transitions. Next, we focus our attention on glioblastoma and with pharmacological and biological considerations, obtain a ranked list of important drug targets in glioblastoms. We perform drug dose response experiments for 22 blood brain barrier penetrant drugs against 3 glioblastoma cell lines. These methods and experimental results inform a construction of a temporal cell state model to predict and experimentally validate combination treatments for certain drugs. We improve an experimental method to perform high throughput western blots and apply the method to discover biochemical interactions among some important proteins involved in temporal cell state transitions. Lastly, we illustrate a method to investigate potential resistance mechanisms in genome scale proteomic data. We hope that methods and results presented here can be adapted and improved upon to help in the discovery of biochemical interactions, capturing cell state transitions and ultimately help predict effective combination therapies for cancer

Cell Modeling

The Air Force is currently developing new products that incorporate a variety of chemicals which may come in contact with product users. To define which chemicals are dangerous to the user, toxicity studies have been performed. However, analysis of toxicity ultimately requires models of the exposed cellular systems. This thesis provides an introduction of how to model and analyze small and large cellular systems. Understanding the underlying behavior of small models and their relation to large systems will lead to a better understanding of how the Air Force should construct intracellular models to assist in future toxicology studies. Developing analysis techniques to include steady state analysis through linearization, and then considering small reaction systems, such as the Brusselator and Schnackenberg models, led to a basic understanding of model behavior. This knowledge was applied to create new models in an effort to begin a transition from previously created models to the construction of models unique to the Air Force. Sensitivity analyses performed on existing systems furthered research efforts by developing knowledge of how systems behave under various initial conditions and perturbations of uncertain constant parameters. Analysis displayed great sensitivity within some models. This analysis was applied to a new model to look for interesting behavior such as oscillatory convergence. The new model was then incorporated into a larger model to determine how its behavior changed with respect to changes in the larger model. This knowledge of how small systems behave in relation to larger systems should help the Air Force to develop and analyze intracellular toxicology models