Mass-balanced randomization of metabolic networks

Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem

JMassBalance: mass-balanced randomization and analysis of metabolic networks

Summary: Analysis of biological networks requires assessing the statistical significance of network-based predictions by using a realistic null model. However, the existing network null model, switch randomization, is unsuitable for metabolic networks, as it does not include physical constraints and generates unrealistic reactions. We present JMassBalance, a tool for mass-balanced randomization and analysis of metabolic networks. The tool allows efficient generation of large sets of randomized networks under the physical constraint of mass balance. In addition, various structural properties of the original and randomized networks can be calculated, facilitating the identification of the salient properties of metabolic networks with a biologically meaningful null model

Assessing the significance of knockout cascades in metabolic networks

Complex networks have been shown to be robust against random structural perturbations, but vulnerable against targeted attacks. Robustness analysis usually simulates the removal of individual or sets of nodes, followed by the assessment of the inflicted damage. For complex metabolic networks, it has been suggested that evolutionary pressure may favor robustness against reaction removal. However, the removal of a reaction and its impact on the network may as well be interpreted as selective regulation of pathway activities, suggesting a tradeoff between the efficiency of regulation and vulnerability. Here, we employ a cascading failure algorithm to simulate the removal of single and pairs of reactions from the metabolic networks of two organisms, and estimate the significance of the results using two different null models: degree preserving and mass-balanced randomization. Our analysis suggests that evolutionary pressure promotes larger cascades of non-viable reactions, and thus favors the ability of efficient metabolic regulation at the expense of robustness

Evolutionary significance of metabolic network properties

Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein–protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone

Topology of molecular interaction networks

Abstract Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks. Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs. Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network 1 topology to generic properties such as robustness, it is used to predict specific functions or phenotypes. Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further

The architecture of regulatory network of metabolism

The thesis focus on the modularity of metabolic network and foremost on the architecture of regulatory network representing direct regulatory interactions between metabolites and enzymes. I focus on the "modularity measure" in my first work. Modularity measure is quantitative measure of network modularity commonly used for module identification. It was showed that algorithms using this measure can produce modules that are composed of two clearly pronounced sub-modules. Maximum size of module for which there is a risk that is is composed of two sub-modules is called resolution limit of modularity measure. In my first work I generalize resolution limit of modularity measure. The generalized version provide insight to the origin of resolution limit in the null-model used by modularity measure. Moreover it is showed that the risk of omitting of sub-modular structures applies for bigger modules than mentioned in the original publication. The second work is focused on the question how does the modular structure of E. coli metabolic network change if we add regulatory interactions. I find that the modularity of modular core of network slightly increase after regulatory edges addition. The modularity increase is significant with respect to randomized ensemble of regulatory networks. Identified modules...Předkládaná disertační práce se zabývá modularitou metabolických sítí a především architekturou regulační sítě metabolismu, která reprezentuje přímé regulační interakce mezi metabolity a enzymy. V první práci se zabývám problematikou tzv. "modularity measure", což je kvantitativní míra modularity sítě používaná pro účely identifikace modulů. Bylo zjištěno, že při maximalizaci této veličiny v síti může dojít k chybnému sloučení dvou jednoznačne vyjádřených modulů v jeden. Maximální velikost modulu u kterého existuje riziko, že je tvořen dvěma moduly je známa jako rozlišovací limit modularity measure. V mé první práci je tento rozlišovací limit zobecněn, což umožňuje nahlédnout jeho podstatu v použití nulového modelu. Zároveň je zde ukázáno, že riziko chybného sloučení existuje i v případě větších modulů, než bylo uváděno v původní práci. Druhá práce je zaměřena na otázku, jak se změní modularita metabolické sítě E.coli po přidání regulačních vazeb. Bylo zde ukázáno, že modularita mírně nicméně signifikantně vzroste, zaměříme-li se na modulární jádro sítě. Identifikované moduly jsou funkčně interpretovatelné jako regulačně autonomí části metabolismu. Zvýšení modularity vzhledem k nulovému modelu lze považovat za nepřímý důsledek potřeby lokální regulace některých částí metabolické sítě. Vznik...Department of Philosophy and History of ScienceKatedra filosofie a dějin přírodních vědFaculty of SciencePřírodovědecká fakult

Nutrient Niche Space of Clostridium difficile Across Susceptible Microbiomes and the Impact of Infection on Metabolism of the Murine Cecal Microbiota

The nosocomial pathogen Clostridium difficile causes an antibiotic-associated diarrheal disease, and largest single cause of hospital-acquired infection as well as gastroenteritis-associated death in the United States. The connection with prior antibiotic therapy is due to the collateral damage induced by these drugs on the community of indigenous bacteria that reside along the gastrointestinal tract. In its healthy state, the gut microbiota prevents the establishment of C. difficile in the gut through the intrinsic property known as colonization resistance. Following a perturbation, like exposure to antibiotics, this community becomes susceptible to colonization by the pathogen and subsequent disease. Most antibiotic classes have been associated with C. difficile infection (CDI) susceptibility; many leading to distinct community structures with unique metabolic profiles stemming from variation in bacterial targets of action. Additionally, a subset of these antibiotics are more closely associated with recurrent or persistent infection. In this thesis I demonstrate that certain susceptible gut communities are more permissive of long-term C. difficile colonization, and that the pathogen has a disproportionate effect on the metabolic activity of communities where persistence occurs. Taxonomic analyses of altered gene expression revealed that this effect consistently impact minority bacterial genera of the community across infection groups. In order to measure the adaptive capacity of C. difficile to these diverse environments, I also generated a genomic/transcriptomic-enabled metabolic modeling platform to assess the differences in nutrient preference of C. difficile across different community contexts. This revealed the pathogen inhabited distinct nutrient niche spaces across susceptible gut environments. My dissertation work has strong implications in future research of targeted pre- and probiotic therapies that mitigate primary or established C. difficile colonization from the gastrointestinal tract.PHDMicrobiology & ImmunologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138521/1/mljenior_1.pd