Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Common principles and best practices for engineering microbiomes

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue hat structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries on the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyze microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy, and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture, and enabling the bioeconomy

Bacterial, particulate, and environmental factors driving E. coli attachment

Currently, 178,048 miles of impaired streams are contaminated due to elevated levels of pathogens or pathogen indicators. While attachment of bacteria to particulates is an important transport mode, understanding of the factors driving these interactions is lacking. Previous studies have indicated bacteria attachment to particulates is influenced by bacterial surface properties, particulate properties, and environmental conditions. The goal of this study was to explore bacterial, particulate, and environmental factors driving E. coli attachment to particulates in the aquatic environment. Specific objectives were: 1) to determine if differences in environmental E. coli cell surface properties are due to extrinsic (environmental) or intrinsic (genomic) properties, or an interaction of the two; 2) to identify the impacts from bacterial and particulate properties on E. coli attachment fractions by constructing statistical models; 3) to elucidate mechanisms of E. coli attachment to particulates in livestock manure. Cell properties including hydrophobicity, zeta potential, net charge, total acidity, and EPS (extracellular polymeric substances) composition were measured for 77 genomically distinct E. coli strains collected from two environmental habitats (stream sediments and water). Meanwhile, attachment assays were constructed using a single E. coli strain and one model particulate (ferrihydrite, Ca-Montmorillonite, or corn stover) with environmentally relevant concentrations. Our results indicated variations between stream sediment E. coli and water E. coli in hydrophobicity, EPS protein and sugar content, net charge, and point of zero charge. The diversity of cell properties was due to interactions of extrinsic and intrinsic properties. Moreover, Generalized Additive Model (GAM) successfully predicted the attachment fractions to Ca-Montmorillonite and corn stover using cell characteristics as predictor variables and net charge had a linear impact on the attachment fractions. Three genomically different beef manure E. coli strains (A, B and C) and one E. coli O157:H7 (ATCC 43888) were analyzed for their attachment to two types of beef manure particles with size smaller than 53 µm: methylene chloride insoluble and soluble. Flow cytometry was employed to measure attachment fractions for 6 different E. coli concentrations and the Freundlich isotherm successfully fitted the attachment data. The results indicated a more heterogeneous mechanism for E. coli attachment to methylene chloride manure particulates

Optimality principles in the regulation of metabolic networks.

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide

A systems biology understanding of protein constraints in the metabolism of budding yeasts

Fermentation technologies, such as bread making and production of alcoholic beverages, have been crucial for development of humanity throughout history. Saccharomyces cerevisiae provides a natural platform for this, due to its capability to transform sugars into ethanol. This, and other yeasts, are now used for production of pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, nutraceuticals, and fuel precursors. In this thesis, different systems biology methods were developed to study interactions between metabolism, enzymatic capabilities, and regulation of gene expression in budding yeasts. In paper I, a study of three different yeast species (S. cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus), exposed to multiple conditions, was carried out to understand their adaptation to environmental stress. Paper II revises the use of genome-scale metabolic models (GEMs) for the study and directed engineering of diverse yeast species. Additionally, 45 GEMs for different yeasts were collected, analyzed, and tested. In paper III, GECKO 2.0, a toolbox for integration of enzymatic constraints and proteomics data into GEMs, was developed and used for reconstruction of enzyme-constrained models (ecGEMs) for three yeast species and model organisms. Proteomics data and ecGEMs were used to further characterize the impact of environmental stress over metabolism of budding yeasts. On paper IV, gene engineering targets for increased accumulation of heme in S. cerevisiae cells were predicted with an ecGEM. Predictions were experimentally validated, yielding a 70-fold increase in intracellular heme. The prediction method was systematized and applied to the production of 102 chemicals in S. cerevisiae (Paper V). Results highlighted general principles for systems metabolic engineering and enabled understanding of the role of protein limitations in bio-based chemical production. Paper VI presents a hybrid model integrating an enzyme-constrained metabolic network, coupled to a gene regulatory model of nutrient-sensing mechanisms in S. cerevisiae. This model improves prediction of protein expression patterns while providing a rational connection between metabolism and the use of nutrients from the environment.This thesis demonstrates that integration of multiple systems biology approaches is valuable for understanding the connection of cell physiology at different levels, and provides tools for directed engineering of cells for the benefit of society

Mathematical Modelling as a Tool for Optimized PHA Production

The potential of poly(hydroxyalkanoates) (PHAs) to replace conventional plastic materials justifies the increasing attention they have drawn both at lab-scale and in industrial biotechnology. The improvement of large-scale productivity and biochemical/genetic properties of producing strains requires mathematical modeling and process/strain optimization procedures. Current models dealing with structurally diversified PHAs, both structured and unstructured, can be divided into formal kinetic, low-structured, dynamic, metabolic (high-structured), cybernetic, neural networks and hybrid models; these attempts are summarized in this review. Characteristic properties of specific groups of models are stressed in light of their benefit to the better understanding of PHA biosynthesis, and their applicability for enhanced productivity. Unfortunately, there is no single type of mathematical model that expresses exactly all the characteristics of producing strains and/or features of industrial-scale plants; in addition, the different requirements for modelling of PHA production by pure cultures or mixed microbial consortia have to be addressed. Therefore, it is crucial to sophisticatedly adapt and fine-tune the modelling approach accordingly to actual processes, as the case arises. For “standard microbial cultivations and everyday practices”, formal kinetic models (for simple cases) and “low-structured” models will be appropriate and of great benefit. They are relatively simple and of low computational demand. To overcome the specific weaknesses of different established model types, some authors use hybrid models. Here, satisfying compromises can be achieved by combining mechanistic, cybernetic, and neural and computational fluid dynamics (CFD) models. Therefore, this hybrid modelling approach appears to constitute the most promising solution to generate a holistic picture of the entire PHA production process, encompassing all the benefits of the original modelling strategies. Complex growth media require a higherdegree of model structuring. For scientific purposes and advanced development of industrial equipment in the future, real systems will be modelled by highly organized hybrid models. All solutions related to modelling PHA production are discussed in this review

Modeling complex cellular systems: from differential equations to constraint-based models

In the beginning of the 20th century, scientists realized the necessity of purifying enzymes to unravel their mechanistic nature. A century and tremendous progresses in the natural sciences later, molecular and systems biology became fundamental pillars of modern biology. Moreover, natural scientists developed an increasing interest in theoretical models. In the first part of my thesis, I present my contribution to the field of studying the dynamics of biological phenomena. I present fundamental issues arising, when neglecting substrate inhibition in kinetic modeling. Furthermore, I describe a model that considers experimental data to simulate the transition of normal proliferating into cellular senescent cells. Since large-scaled models are more comprehensive, they commonly prohibit a mechanistic modeling approach. In order to analyze such models, nevertheless, constraint-based methods proved to be suitable tools. In the second part of my thesis, I contribute three studies to constraint-based modeling. I describe the established concept of elementary flux modes, which resemble non-decomposable and theoretically feasible pathways of metabolic networks. Subsequently, I present the analysis of the nitrogen metabolism network of Chlamydomonas reinhardtii with respect to circadian regulation, which gives rise to about three million elementary flux modes. In the last study, I present a comprehensive work on metabolic costs of amino acid and protein production in Escherichia coli. These costs were manually calculated as well as based on a flux balance analysis of an E. coli genome-scale metabolic model. Both approaches, either dynamic or constraint-based modeling, proved to be suitable strategies to describe biological processes at different levels. Whereas dynamic modeling allowed for a precise description of the temporal behavior of biological species, constraint-based modeling enabled studies, where the complexity of the investigated phenomena prohibits kinetic modeling