Biological Network

Unlike traditional biological research that focuses on a small set of components, systems biology studies the complex interactions among a large number of genes, proteins, and other elements of biological networks and systems. Host-pathogen systems biology examines the interactions between the components of two distinct organisms: a microbial or viral pathogen and its animal host. With the availability of complete genomic sequences of a variety of hosts and pathogens, together with breakthroughs in proteomics, metabolomics, and other experimental areas, the investigation of host-pathogen systems on a multitude of levels of detail has come within reach. This chapter introduces methods and approaches for the analysis of host-pathogen systems. We will particularly emphasize the role of biochemical networks for the study of complex relationships across species boundaries. Although the research area of host-pathogen systems biology spans multiple spatial and temporal scales, we will focus on the molecular and cellular aspects of pathogen-host interactions. We will cover the construction of biochemical networks, the identification of functional response sub-networks, and their comparative network analysis. These methods find application in the identification of host markers and drug targets for further drug development and therapeutic interventions. We will also provide a brief review of other modeling techniques and applications within host-pathogen systems biology, including rule-based modeling of signal transduction pathways, immune system models, and physiological top-down approaches.

Synthetic biology is an emerging field that strives to build increasingly complex biological networks through the integration of molecular biology and engineering. The growth of the field has been supported by progress in the design and construction of synthetic genetic and protein networks. This has led to the possibility of assembling modular components to attain novel biological functions and tools. In addition, these synthetic networks give rise to insights that facilitate the investigation of interactions and phenomena in naturally-occurring networks. Integration of well-characterized biological components into higher order networks requires computational modeling approaches to rationally construct systems that are directed towards a desired outcome. A computational approach would improve the predictability of the underlying mechanisms that would otherwise be difficult to deduce through experimentation alone. The analysis and interpretation of both qualitative and quantitative ...

The emergence of self-similarity and modularity in complex networks raises the fun- damental question of the growth process according to which these structures evolve. The possibility of a unique growth mechanism for biological networks, WWW and the Internet is of interest to the specialist and the laymen alike, as it promises to uncover the universal origins of collective behavior. Here,

Most studies on biological networks until now focus only on pairwise interactions/relationships. However interactions/relationships involving more than two molecules are popular in biology. In this paper, we introduce multivariate mutual information measures to reconstruct multivariate interactions/relationships in biological networks.

Reconstruction of equations of motion from incomplete or noisy data and dimension reduction are two fundamental problems in the study of dynamical systems with many degrees of freedom. For the latter, extensive efforts have been made, but with limited success, to generalize the Zwanzig–Mori projection formalism, originally developed for Hamiltonian systems close to thermodynamic equilibrium, to general non-Hamiltonian systems lacking detailed balance. One difficulty introduced by such systems is the lack of an invariant measure, needed to define a statistical distribution. Based on a recent discovery that a non-Hamiltonian system defined by a set of stochastic differential
equations can be mapped to a Hamiltonian system, we develop such general projection formalism. In the resulting generalized Langevin equations, a set of generalized fluctuation–dissipation relations connect the memory kernel and the random noise terms, analogous to Hamiltonian systems obeying detailed balance. Lacking of these relations restricts previous application of the generalized Langevin formalism. Result of this work may serve as the theoretical basis for further technical developments
on model reconstruction with reduced degrees of freedom. We first use an analytically solvable example to illustrate the formalism and the fluctuation–dissipation relation. Our numerical test on a chemical network with end-product inhibition further demonstrates the validity of the formalism. We suggest that the formalism can find wide applications in scientific modeling. Specifically, we discuss potential applications to biological networks. In particular, the method provides a suitable framework for gaining insights into network properties such as robustness and parameter transferability

Bio Model Analyzer (BMA) is a tool for modeling and analyzing biological networks. Designed with a lightweight graphical user interface, the tool facilitates usage for biologists with no previous knowledge in programming or formal methods. The current implementation analyzes systems to establish stabilization. The results of the analysis|whether they be proofs or counterexamples|are represented visually. This paper describes the approach

Many biological networks contain recurring overrepresented elements, called network motifs. Finding these substructures is a computationally hard task related to graph isomorphism. G-Tries are an efficient data structure, based on multiway trees, capable of efficiently identifying common substructures in a set of subgraphs. They are highly successful in constraining the search space when finding the occurrences of those subgraphs in a larger original graph. This leads to speedups up to 100 times faster than ...

In the past decades, an enormous amount of precious information has been collected about molecular and genetic characteristics of cancer. This knowledge is mainly based on a reductionistic approach, meanwhile cancer is widely recognized to be a ‘system biology disease’. The behavior of complex physiological processes cannot be understood simply by knowing how the parts work in isolation. There is not solely a matter how to integrate all available knowledge in such a way that we can still deal with complexity, but we must be aware that a deeply transformation of the currently accepted oncologic paradigm is urgently needed. We have to think in terms of biological networks: understanding of complex functions may in fact be impossible without taking into consideration influences (rules and constraints) outside of the genome. Systems Biology involves connecting experimental unsupervised multivariate data to mathematical and computational approach than can simulate biologic systems for hypothesis testing or that can account for what it is not known from high-throughput data sets. Metabolomics could establish the requested link between genotype and phenotype, providing informations that ensure an integrated understanding of pathogenic mechanisms and metabolic phenotypes and provide a screening tool for new targeted drug.

Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A deep, recurring question is “What do real graphs look like?” That is, how can we separate real ones from synthetic or real graphs with masked portions? The main contribution of this paper is ShatterPlots, a simple and powerful algorithm to extract patterns from real graphs that help us spot fake/masked graphs.

There is a great need to develop analytical methodology to analyze and to exploit the informationcontained in gene expression data. Because of the large number of genes and the complexityof biological networks, clustering is a useful exploratory technique for analysis of gene expressiondata. Other classical techniques, such as principal component analysis (PCA), have also beenapplied to analyze gene expression data.