Environmental versatility promotes modularity in genome-scale metabolic networks

BACKGROUND: The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. RESULTS: Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. CONCLUSIONS: Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple environments. This organizational principle is insensitive to the environments we consider and to the number of reactions in a metabolic network. Because we observe this principle not just in one or few biological networks, but in large random samples of networks, we propose that it may be a generic principle of metabolic network organization

ModuLand plug-in for Cytoscape: determination of hierarchical layers of overlapping network modules and community centrality

Summary: The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules. Moreover, it identifies several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer. The tool assigns module cores, which predict the function of the whole module, and determines key nodes bridging two or multiple modules. The plug-in has a detailed JAVA-based graphical interface with various colouring options. The ModuLand tool can run on Windows, Linux, or Mac OS. We demonstrate its use on protein structure and metabolic networks. Availability: The plug-in and its user guide can be downloaded freely from: http://www.linkgroup.hu/modules.php. Contact: [email protected] Supplementary information: Supplementary information is available at Bioinformatics online.Comment: 39 pages, 1 figure and a Supplement with 9 figures and 10 table

Degeneracy: a link between evolvability, robustness and complexity in biological systems

A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology. This paper offers a new perspective on the mechanics of evolution and the origins of complexity, robustness, and evolvability. Here we explore the hypothesis that degeneracy, a partial overlap in the functioning of multi-functional components, plays a central role in the evolution and robustness of complex forms. In support of this hypothesis, we present evidence that degeneracy is a fundamental source of robustness, it is intimately tied to multi-scaled complexity, and it establishes conditions that are necessary for system evolvability

Quantifying the benefit of a proteome reserve in fluctuating environments.

The overexpression of proteins is a major burden for fast-growing bacteria. Paradoxically, recent characterization of the proteome of Escherichia coli found many proteins expressed in excess of what appears to be optimal for exponential growth. Here, we quantitatively investigate the possibility that this overexpression constitutes a strategic reserve kept by starving cells to quickly meet demand upon sudden improvement in growth conditions. For cells exposed to repeated famine-and-feast cycles, we derive a simple relation between the duration of feast and the allocation of the ribosomal protein reserve to maximize the overall gain in biomass during the feast

The compositional and evolutionary logic of metabolism

Metabolism displays striking and robust regularities in the forms of modularity and hierarchy, whose composition may be compactly described. This renders metabolic architecture comprehensible as a system, and suggests the order in which layers of that system emerged. Metabolism also serves as the foundation in other hierarchies, at least up to cellular integration including bioenergetics and molecular replication, and trophic ecology. The recapitulation of patterns first seen in metabolism, in these higher levels, suggests metabolism as a source of causation or constraint on many forms of organization in the biosphere. We identify as modules widely reused subsets of chemicals, reactions, or functions, each with a conserved internal structure. At the small molecule substrate level, module boundaries are generally associated with the most complex reaction mechanisms and the most conserved enzymes. Cofactors form a structurally and functionally distinctive control layer over the small-molecule substrate. Complex cofactors are often used at module boundaries of the substrate level, while simpler ones participate in widely used reactions. Cofactor functions thus act as "keys" that incorporate classes of organic reactions within biochemistry. The same modules that organize the compositional diversity of metabolism are argued to have governed long-term evolution. Early evolution of core metabolism, especially carbon-fixation, appears to have required few innovations among a small number of conserved modules, to produce adaptations to simple biogeochemical changes of environment. We demonstrate these features of metabolism at several levels of hierarchy, beginning with the small-molecule substrate and network architecture, continuing with cofactors and key conserved reactions, and culminating in the aggregation of multiple diverse physical and biochemical processes in cells.Comment: 56 pages, 28 figure

Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli.

The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy

C. Elegans Metabolic Gene Regulatory Networks: A Dissertation

In multicellular organisms, determining when and where genes will be expressed is critical for their development and physiology. Transcription factors (TFs) are major specifiers of differential gene expression. By establishing physical contacts with the regulatory elements of their target genes, TFs often determine whether the target genes will be expressed or not. These physical and/or regulatory TF-DNA interactions can be modeled into gene regulatory networks (GRNs), which provide a systems-level view of differential gene expression. Thus far, much of the GRN delineation efforts focused on metazoan development, whereas the organization of GRNs that pertain to systems physiology remains mostly unexplored. My work has focused on delineating the first gene regulatory network of the nematode Caenorhabditis elegans metabolic genes, and investigating how this network relates to the energy homeostasis of the nematode. The resulting metabolic GRN consists of ~70 metabolic genes, 100 TFs and more than 500 protein–DNA interactions. It also includes novel protein-protein interactions involving the metabolic transcriptional cofactor MDT-15 and several TFs that occur in the metabolic GRN. On a global level, we found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs). NHRs form a special class of TFs that can interact with diffusible biomolecules and are well-known regulators of lipid metabolism in other organisms, including humans. Interestingly, NHRs comprise the largest family of TFs in nematodes; the C. elegans genome encodes 284 NHRs, most of which are uncharacterized. In our study, we show that the C. elegans NHRs that we retrieved in the metabolic GRN organize into network modules, and that most of these NHRs function to maintain lipid homeostasis in the nematode. Network modularity has been proposed to facilitate rapid and robust changes in gene expression. Our results suggest that the C. elegans metabolic GRN may have evolved by combining NHR family expansion with the specific modular wiring of NHRs to enable the rapid adaptation of the animal to different environmental cues

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

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