Genome-Scale Computational Approaches to Memory-Intensive Applications in Systems Biology

Author(s):  
Yun Zhang ◽  
F.N. Abu-Khzam ◽  
N.E. Baldwin ◽  
E.J. Chesler ◽  
M.A. Langston ◽  
...  
Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 221
Author(s):  
Ozlem Altay ◽  
Cheng Zhang ◽  
Hasan Turkez ◽  
Jens Nielsen ◽  
Mathias Uhlén ◽  
...  

Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases.


Author(s):  
Hannah Elizabeth Hill ◽  
Salendra Singh ◽  
Kristy Miskimen ◽  
Paula Silverman ◽  
Jill Barnholtz-Sloan ◽  
...  

2019 ◽  
Vol 78 (3) ◽  
pp. 290-304 ◽  
Author(s):  
J. Bernadette Moore

Non-alcoholic fatty liver disease (NAFLD) is now a major public health concern with an estimated prevalence of 25–30% of adults in many countries. Strongly associated with obesity and the metabolic syndrome, the pathogenesis of NAFLD is dependent on complex interactions between genetic and environmental factors that are not completely understood. Weight loss through diet and lifestyle modification underpins clinical management; however, the roles of individual dietary nutrients (e.g. saturated and n-3 fatty acids; fructose, vitamin D, vitamin E) in the pathogenesis or treatment of NAFLD are only partially understood. Systems biology offers valuable interdisciplinary methods that are arguably ideal for application to the studying of chronic diseases such as NAFLD, and the roles of nutrition and diet in their molecular pathogenesis. Although present in silico models are incomplete, computational tools are rapidly evolving and human metabolism can now be simulated at the genome scale. This paper will review NAFLD and its pathogenesis, including the roles of genetics and nutrition in the development and progression of disease. In addition, the paper introduces the concept of systems biology and reviews recent work utilising genome-scale metabolic networks and developing multi-scale models of liver metabolism relevant to NAFLD. A future is envisioned where individual genetic, proteomic and metabolomic information can be integrated computationally with clinical data, yielding mechanistic insight into the pathogenesis of chronic diseases such as NAFLD, and informing personalised nutrition and stratified medicine approaches for improving prognosis.


2012 ◽  
Vol 23 (4) ◽  
pp. 609-616 ◽  
Author(s):  
Murat Iskar ◽  
Georg Zeller ◽  
Xing-Ming Zhao ◽  
Vera van Noort ◽  
Peer Bork

2007 ◽  
Vol 4 (3) ◽  
pp. 252-263 ◽  
Author(s):  
Allyson L. Lister ◽  
Matthew Pocock ◽  
Anil Wipat

Abstract The creation of quantitative, simulatable, Systems Biology Markup Language (SBML) models that accurately simulate the system under study is a time-intensive manual process that requires careful checking. Currently, the rules and constraints of model creation, curation, and annotation are distributed over at least three separate documents: the SBML schema document (XSD), the Systems Biology Ontology (SBO), and the “Structures and Facilities for Model Definition” document. The latter document contains the richest set of constraints on models, and yet it is not amenable to computational processing. We have developed a Web Ontology Language (OWL) knowledge base that integrates these three structure documents, and that contains a representative sample of the information contained within them. This Model Format OWL (MFO) performs both structural and constraint integration and can be reasoned over and validated. SBML Models are represented as individuals of OWL classes, resulting in a single computationally amenable resource for model checking. Knowledge that was only accessible to humans is now explicitly and directly available for computational approaches. The integration of all structural knowledge for SBML models into a single resource creates a new style of model development and checking.


Science ◽  
2013 ◽  
Vol 340 (6137) ◽  
pp. 1220-1223 ◽  
Author(s):  
Roger L. Chang ◽  
Kathleen Andrews ◽  
Donghyuk Kim ◽  
Zhanwen Li ◽  
Adam Godzik ◽  
...  

Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein thermostability in a network context. This analysis allowed the prediction of protein activities that limit network function at superoptimal temperatures and mechanistic interpretations of mutations found in strains adapted to heat. Predicted growth-limiting factors for thermotolerance were validated through nutrient supplementation experiments and defined metabolic sensitivities to heat stress, providing evidence that metabolic enzyme thermostability is rate-limiting at superoptimal temperatures. Inclusion of structural information expanded the content and predictive capability of genome-scale metabolic networks that enable structural systems biology of metabolism.


2021 ◽  
Vol 12 ◽  
Author(s):  
Sander Y. A. Rodenburg ◽  
Michael F. Seidl ◽  
Dick de Ridder ◽  
Francine Govers

Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the “mass-flow” through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host–pathogen interactions, with a specific focus on oomycete plant pathogens and in particular Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.


2014 ◽  
Vol 15 (2) ◽  
pp. 130-159 ◽  
Author(s):  
Ali Najafi ◽  
Gholamreza Bidkhori ◽  
Joseph Bozorgmehr ◽  
Ina Koch ◽  
Ali Masoudi-Nejad

Sign in / Sign up

Export Citation Format

Share Document