A Novel Method for Solving Multi-objective Shortest Path Problem in Respect of Probability Theory

Transportation process or activity can be considered as a multi-objective problem reasonably. However, it is difficult to obtain an absolute shortest path with optimizing the multiple objectives at the same time by means of Pareto approach. In this paper, a novel method for solving multi-objective shortest path problem in respect of probability theory is developed, which aims to get the rational solution of multi-objective shortest path problem. Analogically, each objective of the shortest path problem is taken as an individual event, thus the concurrent optimization of many objectives equals to the joint event of simultaneous occurrence of the multiple events, and therefore the simultaneous optimization of multiple objectives can be solved on basis of probability theory rationally. The partial favorable probability of each objective of every scheme (routine) is evaluated according to the actual preference degree of the utility indicator of the objective. Moreover, the product of all partial favorable probabilities of the utility of objective of each scheme (routine) casts the total favorable probability of the corresponding scheme (routine), which results in the decisively unique indicator of the scheme (routine) in the multi-objective shortest path problem in the point of view of system theory. Thus, the optimum solution of the multi-objective shortest path problem is the scheme (routine) with highest total favorable probability. Finally, an application example is given to illuminate the approach

Structure and Dynamics of the Canine (Canis familiaris) Nasal Microbiome

The diverse community of bacteria living within and on host organisms, known as the microbiome, has an important role in maintaining host health. Dysbiosis, known as a change in the healthy community of the microbiome, has been associated with a number of diseases across host organisms and body sites including asthma, pneumonia, gastrointestinal disorders, obesity, and depression. Because of the close connection with host health, understanding what processes are driving the structure of the healthy microbiome and how the microbial community changes with disease and environmental perturbations are crucial in agricultural, medical, and potentially conservation applications. From the perspective of the microbiome these applications represent potential ecosystem services and understanding what drives them encapsulates enduring questions in ecology, concerning ecosystem stability and species coexistence — the processes that underpin biodiversity over space and time and in response to perturbations and (anthropogenic) change. In my research I explore a novel field system for addressing these questions consisting of the nasal microbiomes of canines (Canis familiaris) living within the Oregon Humane Society. The overall goal is to elucidate the processes driving the structure of the nasal microbiome during health and disease, and to understand how host demography and pathogen dynamics may influence diversity and stability of microbiomes. In this dissertation I explore several mechanisms that may influence the canine nasal microbiome diversity within and among hosts, including: infection with respiratory pathogens; host demographic variables such as age; host contact patterns such as whether dogs were housed in the same kennel or area of the shelter; and microbe-microbe interactions, including co-occurrence that may represent commensalism or mutualism, and negatively associated taxa possibly representing competition or competitive exclusion. First, I characterize the differences between the healthy microbiome and the microbiome of dogs experiencing respiratory disease as well as the pathogen community in both healthy and sick dogs. Then I explore differences between hosts that explain community variation based on host demographics combined with community assembly theory (defined as the process by which species colonize and interact to form local communities) to understand how the healthy microbiome changes throughout an organism’s life both long-term (comparing young and adult canines) and short term (via contact with other hosts). Finally, I use network analysis methods to understand how microbial interactions could structure the microbiome and identify co-occurrence patterns and influential taxa whose removal or change in abundance may have consequences on the rest of the microbiome. This research helps provide a better understanding of how the microbiome is formed throughout the lives of the host and how it changes as a result of the environment, invading pathogens, and the host itself. Understanding how the healthy microbiome is structured and maintained during health and disease is crucial as we continue to seek treatments that may involve manipulating the microbiome for complex diseases, such as those affecting the respiratory tract of animals

Bioinformatic pipelines to reconstruct and analyse intercellular and hostmicrobe interactions affecting epithelial signalling pathways

The epithelium segregates microorganisms from the immune system through tightly connected cells. The epithelial barrier maintains the integrity of the body, and the microbiome influences this through host-microbe interactions. Therefore its composition has an impact on the host's physiological processes. Disruption in the microbiome composition leads to an impaired epithelial layer. As a consequence, the cell-cell interactions between the epithelium and immune cells will be altered, contributing to inflammation. In this thesis, I examined the interconnectivity of the microbiome, epithelium and immune system in the gastrointestinal tract focusing on the oral cavity and gut in healthy and diseased conditions. I combined multi-omics data with network biology approaches to develop computational pipelines to study host-microbe and cell-cell connections. I used network propagation algorithms to reconstruct intracellular signalling and identify downstream pathways affected by the altered microbiome composition or cell-cell connections. I studied inflammation-related conditions in the oral cavity (periodontitis) and gut (inflammatory bowel disease (IBD)) to reveal the contribution of interspecies and intercellular interactions to diseases. I inferred hostmicrobe protein-protein interaction (HM-PPI) networks between healthy gum-/periodontitisrelated bacteria communities and epithelium, and found altered HM-PPIs during inflammation. I connected the epithelial cells to dendritic cells and identified the Toll-like receptor (TLR) pathway as a potential driver of the inflammation in diseased gingiva. While in the oral cavity I focused on complex microbial communities and their impact on one cell type, I discovered the direct effect of gut commensal bacteria on several immune cells in IBD. This study observed the cell-specific effect of Bacteroides thetaiotaomicron on TLR signalling. The pipelines I developed offer potentially interesting connections that aid detailed mechanistic insight into the relationship between the microbiome, epithelial barrier and immune system. These systems-level analysis tools facilitate the understanding of how microbial proteins may be of therapeutic value in inflammatory diseases

Shortest path counting in probabilistic biological networks

Abstract Background Biological regulatory networks, representing the interactions between genes and their products, control almost every biological activity in the cell. Shortest path search is critical to apprehend the structure of these networks, and to detect their key components. Counting the number of shortest paths between pairs of genes in biological networks is a polynomial time problem. The fact that biological interactions are uncertain events however drastically complicates the problem, as it makes the topology of a given network uncertain. Results In this paper, we develop a novel method to count the number of shortest paths between two nodes in probabilistic networks. Unlike earlier approaches, which uses the shortest path counting methods that are specifically designed for deterministic networks, our method builds a new mathematical model to express and compute the number of shortest paths. We prove the correctness of this model. Conclusions We compare our novel method to three existing shortest path counting methods on synthetic and real gene regulatory networks. Our experiments demonstrate that our method is scalable, and it outperforms the existing methods in accuracy. Application of our shortest path counting method to detect communities in probabilistic networks shows that our method successfully finds communities in probabilistic networks. Moreover, our experiments on cell cycle pathway among different cancer types exhibit that our method helps in uncovering key functional characteristics of biological networks