Structural modelling and robustness analysis of complex metabolic networks and signal transduction cascades

The dissertation covers the topic of structural robustness of metabolic networks on the basis of the concept of elementary flux modes (EFMs). It is shown that the number of EFMs does not reflect the topology of a network sufficiently. Thus, new methods are developed to determine the structural robustness of metabolic networks. These methods are based on systematic in-silico knockouts and the subsequent calculation of dropped out EFMs. Thereby, together with single knockouts also double and multiple knockouts can be used. After evaluation of these methods they are applied to metabolic networks of human erythrocyte and hepatocyte as well as to a metabolic network of Escherichia coli (E. coli). It is found that the erythrocyte has the lowest structural robustness, followed by the hepatocyte and E. coli. These results coincide very well with the circumstance that human erythrocyte and hepatocyte and E. coli are able to adapt to conditions with increasing diversity. In a further part of the dissertation the concept of EFMs is expanded to signal transduction pathways consisting of kinase cascades. The concept of EFMs is based on the steady-state condition for metabolic pathways. It is shown that under certain circumstances this steady-state condition also holds for signalling cascades. Furthermore, it is shown that it is possible to deduce minimal conditions for signal transduction without knowledge about the kinetics involved. On the basis of these assumptions it is possible to calculate EFMs for signalling cascades. But due to the fact that these EFMs do no longer just have mass flux but also information flux, they are now called elementary signalling modes (ESMs).Die Dissertation behandelt die strukturelle Robustheit von metabolischen Netzwerken auf der Basis des Konzepts der elementaren Flussmoden (EFMen). Es wird gezeigt, dass die Anzahl der EFMen die Topologie eines metabolischen Netzes nicht ausreichend widerspiegelt. Darauf aufbauend werden neue Methoden entwickelt, um die strukturelle Robustheit metabolischer Netze zu bestimmen. Diese Methoden beruhen auf systematischen in-silico-Knockouts und der anschließenden Bestimmung des Anteils an weggefallenen EFMen. Dabei können neben Einfach-Knockouts auch Doppel- oder Mehrfach-Knockouts verwendet werden. Nach der Evaluierung werden diese Methoden auf metabolische Netzwerke des menschlichen Erythrozyten und Hepatozyten, sowie des Bakteriums Escherichia coli (E. coli) angewendet. Es zeigt sich, dass der Erythrozyt die im Vergleich geringste strukturelle Robustheit besitzt, gefolgt vom Hepatozyten und E. coli. Diese Ergebnisse stimmen sehr gut mit der Beobachtung überein, dass sich die menschlichen Erythrozyten und Hepatozyten, sowie E. coli an zunehmend verschiedene Bedingungen anpassen können. In einem weiteren Teil der Dissertation wird das Konzept der EFMen auf Signaltransduktionswege bestehend aus Kinase-Kaskaden erweitert. Das Konzept der EFMen beruht auf der Annahme eines quasi-stationären Zustands für metabolische Netzwerke. Es wird gezeigt, dass dieser quasi-stationäre Zustand unter bestimmten Bedingungen auch in Signal-Kaskaden angenommen werden kann. Weiterhin wird gezeigt, dass man ohne Kenntnis der beteiligten Kinetiken Minimalbedingungen für die Signalweiterleitung ableiten kann. Auf Basis dieser Annahmen lassen sich für Signal-Kaskaden EFMen berechnen. Aber aufgrund der Tatsache, dass sie nicht mehr nur Masse-, sondern auch Informationsfluss beschreiben, werden sie nun als elementare Signalmoden (ESMen) bezeichnet

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Convection-Enhanced Delivery of Macromolecules to the Brain Using Electrokinetic Transport

Electrokinetic transport in brain tissue represents the movement of molecules due to an applied electric field and the interplay between the electrophoretic and electroosmotic velocities that are developed. This dissertation provides a framework for understanding electrokinetic transport and how it may be utilized for short-distance ejections, relevant to capillary iontophoresis, and long-distance infusions, for the clinical management of malignant brain tumors as a novel convection-enhanced drug delivery system.In particular, electrokinetic transport was first analyzed in a series of poly(acrylamide-co-acrylic acid) hydrogels that demonstrated varying electroosmotic velocities. Moreover, a hydrogel was synthesized to mimic the electrokinetic properties of organotypic hippocampal slice cultures (OHSC), as a surrogate for brain tissue. Short- and long-distance capillary infusions of molecules into the hydrogels and OHSC provided a framework to understand the relevant phenomena, such as the effect of varying the capillary tip size, applied electrical current, ζ-potential of the capillary or the outside matrix, infusion time, tortuosity, and properties of the solute (including molecular weight and electrophoretic mobility). Control of the directional transport of molecules was also demonstrated over a distance of several hundred micrometers to millimeters. Finally, electrokinetic infusions were conducted in vivo in the adult rat brain, with results compared to those of pressure-driven infusions.The experiments and results described in this dissertation provide a foundation for further development, by presenting a methodical means to increase the ejection profile and attain clinically relevant penetration distances while minimizing adverse effects to the brain tissue, including from the electric field itself. The rate of electrokinetic transport is greater than the rate of diffusion, and therefore it represents a novel form of convection-enhanced drug delivery system

Machine Learning for Modelling Tissue Distribution of Drugs and the Impact of Transporters

The ability to predict human pharmacokinetics in early stages of drug development is of paramount importance to prevent late stage attrition as well as in managing toxicity. This thesis explores the machine learning modelling of one of the main pharmacokinetics parameters that determines the therapeutic success of a drug - volume of distribution. In order to do so, a variety of physiological phenomena with known mechanisms of impact on drug distribution were considered as input features during the modelling of volume of distribution namely, Solute Carriers-mediated uptake and ATP-binding Cassette-mediated efflux, drug-induced phospholipidosis and plasma protein binding. These were paired with molecular descriptors to provide both chemical and biological information to the building of the predictive models. Since biological data used as input is limited, prior to modelling volume of distribution, the various types of physiological descriptors were also modelled. Here, a focus was placed on harnessing the information contained in correlations within the two transporter families, which was done by using multi-label classification. The application of such approach to transporter data is very recent and its use to model Solute Carriers data, for example, is reported here for the first time. On both transporter families, there was evidence that accounting for correlations between transporters offers useful information that is not portrayed by molecular descriptors. This effort also allowed uncovering new potential links between members of the Solute Carriers family, which are not obvious from a purely physiological standpoint. The models created for the different physiological parameters were then used to predict these parameters and fill in the gaps in the available experimental data, and the resulting merging of experimental and predicted data was used to model volume of distribution. This exercise improved the accuracy of volume of distribution models, and the generated models incorporated a wide variety of the different physiological descriptors supplied along with molecular features. The use of most of these physiological descriptors in the modelling of distribution is unprecedented, which is one of the main novelty points of this thesis. Additionally, as a parallel complementary work, a new method to characterize the predictive reliability of machine learning classification model was proposed, and an in depth analysis of mispredictions, their trends and causes was carried out, using one of the transporter models as example. This is an important complement to the main body of work in this thesis, as predictive performance is necessarily tied to prediction reliability

A multi-modality approach for enhancing the diagnosis of cholangiocarcinoma

Background: Cholangiocarcinoma (CC) is a malignancy of the bile ducts and mortality is high as patients present too late for curative surgery. In most cases of CC the aetiology is unknown, whilst diagnosis and staging are challenging. The hepatobiliary system excretes carcinogenic toxins and genetic mutations in biliary transporters lead to dysfunction and cholestasis, potentially contributing to cholangiocarcinogenesis. Polymorphisms in the NKG2D receptor have previously been associated with CC in primary sclerosing cholangitis (PSC). Such a role has not been investigated in sporadic CC. CC is difficult to diagnose, particularly in those with PSC. The transition from benign to malignant biliary disease is likely to be reflected in changes to the plasma proteome. However, current plasma biomarkers do not reliably distinguish benign from malignant biliary strictures. Elevation of neutrophil gelatinase-associated lipocalin (NGAL) has been demonstrated in the bile of patients with CC but has not been investigated as a plasma protein biomarker. Staging of CC is inaccurate, with only a minority of operated patients cured. Higher resolution MRI would improve diagnosis and staging. The work presented in this thesis represents a multimodality approach to enhance the diagnosis of CC: Genetic studies: Genetic variation in major biliary transporter proteins, and the NKG2D receptor, were investigated. Single nucleotide polymorphisms (SNPs) in candidate genes were selected using HapMap. DNA from 173 CC patients and 265 healthy controls was genotyped. SNPs in ABCB11, MDR3 and ATP8B1 were nominally associated with altered susceptibility to CC, suggesting a potential role in cholangiocarcinogenesis. The previous association of NKG2D variation with CC in PSC was not replicated in sporadic CC, suggesting a possible difference in pathogenesis. Protein studies: Plasma from subjects with CC, benign disease, and from healthy controls was studied. Two proteomic techniques, liquid chromatography-tandem mass spectrometry (LCMS/ MS) and surfaced enhanced laser desorption ionization time-of-flight MS (SELDITOF MS), were utilised. Differentially expressed proteins were identified where possible. LC-MS/MS fully identified six proteins that were differentially expressed in CC compared to gall stone disease patients. SELDI-TOF MS identified seven m/z peaks that showed significant utility in discriminating CC from PSC controls. An ELISA approach was used to study plasma NGAL levels in CC. Although differentially expressed between CC and healthy control groups, the utility of NGAL in discriminating CC from PSC was limited. Imaging studies: An endoscope-mounted MR coil and intraductal MR detector coil were developed. Quantitative resolution and signal-to-noise-ratio (SNR) testing, and qualitative tissue discrimination appraisal, were undertaken. Sub-0.7mm resolution and excellent SNRs have been demonstrated. High-resolution has been demonstrated in imaged tissue. Imaging with the new devices compares favourably with endoscopic ultrasound imaging

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

The insulin-degrading enzyme: from molecular evolution and subcellular localization to new roles in microglial physiology

La enfermedad de Alzheimer y la diabetes mellitus son dos patologías crónicas con un alarmante incremento en su incidencia y prevalencia a nivel mundial. Se ha propuesto el término "diabetes tipo 3" para describir la hipótesis de que el Alzheimer está causado por un tipo de resistencia a insulina que ocurre específicamente en el cerebro. La enzima degradadora de insulina (IDE) es una metaloproteasa altamente expresada en el cerebro, capaz de degradar in vitro no solo la insulina sino también los péptidos beta amiloides, lo cual convierte a esta proteína een una buena diana para estudiar la diabetes tipo 3. Los resultados presentados en esta Tesis revelan nuevas propiedades biológicas y funciones fisiológicas de IDE en el sistema nervioso, particularmente en la microglía, en la que modula su respuesta multidimensional a diferentes condiciones relevantes en la patogénesis del Alzheimer y la diabetes.Departamento de Bioquímica y Biología Molecular y FisiologíaDoctorado en Investigación Biomédic