Bioinformatics meets systems biology.
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Computational Biology↗
Biomedical subjects
Oliver Ebenhöh
Publications and source records attributed to Oliver Ebenhöh.
Expanding metabolic networks: scopes of compounds, robustness, and evolution.
A new method for the mathematical analysis of large metabolic networks is presented. Based on the fact that the occurrence of a metabolic reaction generally requires the existence of other reactions providing its substrates, series of metabolic networks are constructed. In each step of the corresponding expansion process those reactions are incorporated whose substrates are made available by the networks of the previous generations. The method is applied to the set of all metabolic reactions included in the KEGG database. Starting with one or more seed compounds, the expansion results in a final network whose compounds define the scope of the seed. Scopes of all metabolic compounds are calculated and it is shown that large parts of cellular metabolism can be considered as the combined scope of simple building blocks. Analyses of various expansion processes reveal crucial metabolites whose incorporation allows for the increase in network complexity. Among these metabolites are common cofactors such as NAD+, ATP, and coenzyme A. We demonstrate that the outcome of network expansion is in general very robust against elimination of single or few reactions. There exist, however, crucial reactions whose elimination results in a dramatic reduction of scope sizes. It is hypothesized that the expansion process displays characteristics of the evolution of metabolism such as the temporal order of the emergence of metabolic pathways.
Adenosine Triphosphate↗
A cross species comparison of metabolic network functions.
We compare a large number of organisms with respect to their metabolic network functions. We measure such functions in terms of the synthesizing capacity of a network when it is provided with a few small chemical substances as external resources. We call this measure the scope and show that it is generally robust against structural alterations of the reaction network. Organisms can be separated into two groups, one with a small and one with a large scope. Networks with a high synthesizing capacity also show a high degree of robustness against structural changes, indicating that this network function has been a target in the evolutionary past of the corresponding organisms. A comparison between structural and functional similarities reveals that organisms with a similar structure do not necessarily show similar biological functions. The presented concepts allow for a systematic investigation of structure-function relationships of metabolic networks and may put forth valuable hints on the evolution of metabolic pathways.
Algorithms↗
Structural analysis of expanding metabolic networks.
Methods are developed for structural analysis of metabolic networks expanding in size. Expansion proceeds in consecutive generations in which new reactions are attached to the network produced in the previous stage. Different rules are applied resulting in various modes of expansion. Expansion is performed on the set of glycolytic reactions as well as on a very large set of reactions taken from the KEGG database. It is shown that reactions and compounds strongly differ in the generation in which they are attached to the network allowing conclusions for the temporal order of the acquisition during network evolution. The expansion provides efficient tools for detecting new structural characteristics such as substrate-product relationships over long distances.
Databases, Factual↗
Stoichiometric design of metabolic networks: multifunctionality, clusters, optimization, weak and strong robustness.
Starting from a limited set of reactions describing changes in the carbon skeleton of biochemical compounds complete sets of metabolic networks are constructed. The networks are characterized by the number and types of participating reactions. Elementary networks are defined by the condition that a specific chemical conversion can be performed by a set of given reactions and that this ability will be lost by elimination of any of these reactions. Groups of networks are identified with respect to their ability to perform a certain number of metabolic conversions in an elementary way which are called the network's functions. The number of the network functions defines the degree of multifunctionality. Transitions between networks and mutations of networks are defined by exchanges of single reactions. Different mutations exist such as gain or loss of function mutations and neutral mutations. Based on these mutations neighbourhood relations between networks are established which are described in a graph theoretical way. Basic properties of these graphs are determined such as diameter, connectedness, distance distribution of pairs of vertices. A concept is developed to quantify the robustness of networks against changes in their stoichiometry where we distinguish between strong and weak robustness. Evolutionary algorithms are applied to study the development of network populations under constant and time dependent environmental conditions. It is shown that the populations evolve toward clusters of networks performing a common function and which are closely neighboured. Under changing environmental conditions multifunctional networks prove to be optimal and will be selected.
Algorithms↗