Properties of Metabolic Networks: Structure versus Function

Mahadevan, Radhakrishnan; Palsson, Berrnhard O.

doi:10.1529/biophysj.104.055723

Cited by 75 publications

(54 citation statements)

References 9 publications

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“…We expect these species to typically be specialists, rather than generalists. However, experience with other types of biological networks indicates that the number of interactions alone is not a perfect indicator of a node's criticality for the system's stability (58)(59)(60).…”

Section: Discussionmentioning

confidence: 99%

A network model for plant–pollinator community assembly

Campbell¹,

Yang

Albert

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

105

124

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Community assembly models, usually constructed for food webs, are an important component of our understanding of how ecological communities are formed. However, models for mutualistic community assembly are still needed, especially because these communities are experiencing significant anthropogenic disturbances that affect their biodiversity. Here, we present a unique network model that simulates the colonization and extinction process of mutualistic community assembly. We generate regional source pools of species interaction networks on the basis of statistical properties reported in the literature. We develop a dynamic synchronous Boolean framework to simulate, with few free parameters, the dynamics of new mutualistic community formation from the regional source pool. This approach allows us to deterministically map out every possible trajectory of community formation. This level of detail is rarely observed in other analytic approaches and allows for thorough analysis of the dynamical properties of community formation. As for food web assembly, we find that the number of stable communities is quite low, and the composition of the source pool influences the abundance and nature of community outcomes. However, in contrast to food web assembly, stable mutualistic communities form rapidly. Small communities with minor fluctuations in species presence/absence (selfsimilar limit cycles) are the most common community outcome. The unique application of this Boolean network approach to the study of mutualistic community assembly offers a great opportunity to improve our understanding of these critical communities.mutualism | transition graph | bipartite

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Section: Discussionmentioning

confidence: 99%

A network model for plant–pollinator community assembly

Campbell¹,

Yang

Albert

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

105

124

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show abstract

“…It is possible to partially 'fix' this problem by a priori eliminating the carriers from graphs, but then the resulting graphs have low variability in metabolite node degree sequence and thus no longer have power laws, the defining feature of SF networks. Recent experimental results in [11] further shows that the degree of the metabolite nodes is not necessarily correlated with its lethality.…”

Section: Sr Topologymentioning

confidence: 99%

Highly optimised global organisation of metabolic networks

Tanaka¹,

Csete

Doyle

2005

IEE Proc. Syst. Biol.

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High-level, mathematically precise descriptions of the global organisation of complex metabolic networks are necessary for understanding the global structure of metabolic networks, the interpretation and integration of large amounts of biologic data (sequences, various -omics) and ultimately for rational design of therapies for disease processes. Metabolic networks are highly organised to execute their function efficiently while tolerating wide variation in their environment. These networks are constrained by physical requirements (e.g. conservation of energy, redox and small moieties) but are also remarkably robust and evolvable. The authors use well-known features of the stoichiometry of bacterial metabolic networks to demonstrate how network architecture facilitates such capabilities, and to develop a minimal abstract metabolism which incorporates the known features of the stoichiometry and respects the constraints on enzymes and reactions. This model shows that the essential functionality and constraints drive the tradeoffs between robustness and fragility, as well as the large-scale structure and organisation of the whole network, particularly high variability. The authors emphasise how domainspecific constraints and tradeoffs imposed by the environment are important factors in shaping stoichiometry. Importantly, the consequence of these highly organised tradeoffs and tolerances is an architecture that has a highly structured modularity that is self-dissimilar and scale-rich. IntroductionMetabolic networks, which have been extensively studied for decades, are emblematic of how evolution has sculpted biologic systems for optimal function. In addition to unambiguous functional descriptions of core metabolism, this conserved network has been recently described in detail in terms of its stoichiometry (mass and energy balance). A higher level, mathematically defined description of the global organisation of complex metabolic networks is critical for a deep understanding of metabolism, from the interpretation of huge amounts of biologic data (sequences, various -omics) to design of therapies for disease processes. The stakes are high for obtaining the big picture right: biologic data plugged into a distorted model or interpreted in the context of a flawed universal law propagates misinterpretations. In flux analyses [1], stoichiometry is considered as a constraint, and fluxes are optimised to satisfy a global objective, typically growth. Previous studies, however, have not directly addressed whether the stoichiometry itself is highly optimal or organised in any sense and contributes to the origins and purpose of complexity in biological networks. Yet biochemistry textbooks describe metabolism as having evolved to be 'highly integrated' with the appearance of a 'coherent design' [2]. Here we explore both important 'design' (with no implication of a 'designer') features of metabolism and the sense in which stoichiometry itself has highly organised and optimised tolerances and tradeoffs (HOT) [3] for fun...

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“…This point has been experimentally corroborated in S. cerevisiae, where the severity of a gene knockout has been shown to correlate with the number of interactions in which the gene's products participate (Jeong et al, 2001;Said et al, 2004). High degree is a practical but nevertheless insufficient predictor of functional importance, as there are several examples of low-degree nodes that are critical for certain outcomes (Holme et al, 2003;Almaas et al, 2005;Mahadevan and Palsson, 2005;Li et al, 2006). Ultimately, a high-precision prediction of functionally important nodes will need to take into account the biological identity of the nodes and the synergistic and dynamic aspects of the interactions and will therefore require significantly more input information than what it currently available for most interaction networks.…”

Section: Network Analysismentioning

confidence: 84%

Network Inference, Analysis, and Modeling in Systems Biology

2007

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Cells use signaling and regulatory pathways connecting numerous constituents, such as DNA, RNA, proteins, and small molecules, to coordinate multiple functions, allowing them to adapt to changing environments. High-throughput experimental methods enable the measurement of expression levels for thousands of genes and the determination of thousands of protein-protein or protein-DNA interactions. It is increasingly recognized that theoretical methods, such as statistical inference, graph analysis, and dynamic modeling, are needed to make sense of this abundance of information. This perspective argues that theoretical methods and models are most useful if they lead to novel biological predictions and reviews biological predictions arising from three systems biology topics: graph inference (i.e., reconstructing the network of interactions among a set of biological entities), graph analysis (i.e., mining the information content of the network), and dynamic network modeling (i.e., connecting the interaction network to the dynamic behavior of the system). The methods and principles discussed in this perspective are generally applicable, and the examples were selected from plant biology wherever possible. INTRODUCTIONTo understand the function of a cell or of higher units of biological organization, often it is beneficial to conceptualize them as systems of interacting elements. For such a systems-level description (which represents the main goal of systems biology), one needs to know (1) the identity of the components that constitute the biological system; (2) the dynamic behavior of these components (i.e., how their abundance or activity changes over time in various conditions); and (3) the interactions among these components (Kitano, 2002). Ultimately, this information can be combined into a model that is not only consistent with current knowledge but provides new insights and predictions, such as the behavior of the system in conditions that were previously unexplored.The origins of systems biology can be traced back to systems theory, a line of inquiry based on the assumptions that all phenomena can be viewed as a web of relationships among elements, and all systems can be handled by a common set of methods (von Bertalanffy, 1968;Weinberg, 1975;Bogdanov, 1980;Heinrich and Schuster, 1996;Francois, 1999;Voit, 2000). Early attempts at systems-level understanding of biology suffered from inadequate data on which to base the theories and models; however, the recent advent of high-throughput technologies brought an abundance of data on system elements and interactions, leading to a revival of systems biology.In some cases, the organization of the network of interactions underlying a biological system is straightforward (e.g., a linear chain of interactions), while in other cases a more formal representation, offered by mathematical graph theory (Bollobá s, 1979), is required. The simplest possible graph representation reduces the system's elements to graph nodes (also called vertices) and reduces their pairwise relationsh...

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Properties of Metabolic Networks: Structure versus Function

Cited by 75 publications

References 9 publications

A network model for plant–pollinator community assembly

A network model for plant–pollinator community assembly

Highly optimised global organisation of metabolic networks

Network Inference, Analysis, and Modeling in Systems Biology

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