Seeing the forest for the trees : retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed

Modular Composition of Gene Transcription Networks

Predicting the dynamic behavior of a large network from that of the composing modules is a central problem in systems and synthetic biology. Yet, this predictive ability is still largely missing because modules display context-dependent behavior. One cause of context-dependence is retroactivity, a phenomenon similar to loading that influences in non-trivial ways the dynamic performance of a module upon connection to other modules. Here, we establish an analysis framework for gene transcription networks that explicitly accounts for retroactivity. Specifically, a module's key properties are encoded by three retroactivity matrices: internal, scaling, and mixing retroactivity. All of them have a physical interpretation and can be computed from macroscopic parameters (dissociation constants and promoter concentrations) and from the modules' topology. The internal retroactivity quantifies the effect of intramodular connections on an isolated module's dynamics. The scaling and mixing retroactivity establish how intermodular connections change the dynamics of connected modules. Based on these matrices and on the dynamics of modules in isolation, we can accurately predict how loading will affect the behavior of an arbitrary interconnection of modules. We illustrate implications of internal, scaling, and mixing retroactivity on the performance of recurrent network motifs, including negative autoregulation, combinatorial regulation, two-gene clocks, the toggle switch, and the single-input motif. We further provide a quantitative metric that determines how robust the dynamic behavior of a module is to interconnection with other modules. This metric can be employed both to evaluate the extent of modularity of natural networks and to establish concrete design guidelines to minimize retroactivity between modules in synthetic systems.United States. Air Force Office of Scientific Research (FA9550-12-1-0129

Towards light based dynamic control of synthetic biological systems

For the field of synthetic biology, the adaptation of principles, from the well established traditional engineering disciplines, like mechanical and electrical engineering, in order to realise complex synthetic biological circuits, is an intriguing prospect. These principles can enable a forward engineering, rational design and implementation approach, where a system's properties can be predicted or designed in silico followed by the manufacturing of the in vivo system, that can be tested, used or redesigned in the most efficient possible way. Achieving control over these circuits, is one of the important topics of the field, for these applications to become robust and render useful functions applicable to energy, medicine, pharmaceuticals and agriculture industries. In this work, I attempt to explore light, as a promising control 'dial' for synthetic circuitry. Light is fast, economic compared to chemicals, it can be interfaced with electronics, it is reversible in its effect and can be applied at a fine spatio-temporal resolution. These characteristics, are absent from the classically used chemical inducers, meaning that light, can open new possibilities for the user to control synthetic systems, or even facilitate the cell to cell communication, within population based networks. This work, is a contribution towards harnessing the advantages of light, for achieving control over synthetic circuits. More specifically, I start with the detailed theoretical and experimental study of the Cph8 two component system, a synthetic chimeric receptor which is responsive to red light. This is done, in order to develop a sufficient theoretical understanding of it, through detailed mechanistic modelling, in order to connect the specific system with the toggle switch and the dual feedback oscillator, in an optimal way and achieve control of these devices through light. The developed model, was able to highlight the main aspects and mechanisms inherent to its structure, describe most of the observations from the experimental system, to also make quantitative predictions. The second part of this work, was the development of novel promoters, that can be regulated by a commonly used transcription factor, such as LacI, but also, light responsive regulators like OmpR and CcaR. This yielded a direct way to integrate light and chemical inputs, into a single output, while the dual regulation, allowed to connect and modulate the toggle switch without the need of additional transcription factors. The latter, a light tuneable toggle switch, showed indications that it can function as a memory controller that can be reset by light. Finally, I show the design and modelling of a light tuneable dual feedback oscillator, where light of one wavelength can be used to tune the amplitude, while another wavelength can tune the period. The developed models and synthetic circuits are expected to contribute towards implementing finely tuned and controlled synthetic circuits through light.Open Acces

A role for detailed assessment of hippocampal function in studies of Alzheimer’s Disease

The hippocampus is one of the first cortical regions to exhibit Alzheimer’s Disease (AD) pathology. The spatially-related firing of hippocampal place cells provides the cellular basis for spatial memory, and this is impaired relatively early in AD, yet few studies examine place cell activity in AD mouse models. Furthermore, current spatial navigation paradigms for rodents are not suited to tracking the progressive impairment seen in AD. This project aimed to address these gaps; the results provide initial support for the hypothesis that AD pathology disrupts hippocampal function which manifests as altered place cell activity and spatial behaviour. Chapter 3 outlines experiments validating a novel behavioural test of hippocampal function, the ‘Honeycomb Maze’, specifically designed to overcome the limitations of other tasks. Wild-type rats and mice rapidly learnt to navigate to a hidden goal, and a lesion study demonstrated the key contribution made by the hippocampus. Task difficulty was scalable through altering maze parameters, with difficult choices exhibiting a greater reliance on hippocampal processing. The findings suggest the Honeycomb Maze provides a reliable means of assessing hippocampal function in rodents and is well suited for application to studies of AD. Chapter 4 provides an in-depth characterisation of hippocampal pyramidal cell activity in an APP knock-in model of AD. Electrophysiological recordings were performed in the left CA1 subregion of four 15-month-old, freely moving, APPNL-G-F mice and four age-matched wild-type controls. Significantly fewer APPNL-G-F pyramidal cells exhibited spatial firing, and deficits were present in rate and temporal coding of spatial information. APPNL-G-F spatial cells had lower spatial information content, larger place fields, reduced phase-locking to the theta rhythm of the local field potential, and a reduction in theta phase precession. Despite the small sample size, a positive correlation was identified between amyloid β plaque burden and pyramidal cell spatial information in APPNL-G-F mice

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers