| 1. |
- Holme, Petter, et al.
(författare)
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Atmospheric Reaction Systems as Null-Models to Identify Structural Traces of Evolution in Metabolism
- 2011
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 6:5, s. e19759-
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Tidskriftsartikel (refereegranskat)abstract
- The metabolism is the motor behind the biological complexity of an organism. One problem of characterizing its large-scale structure is that it is hard to know what to compare it to. All chemical reaction systems are shaped by the same physics that gives molecules their stability and affinity to react. These fundamental factors cannot be captured by standard null-models based on randomization. The unique property of organismal metabolism is that it is controlled, to some extent, by an enzymatic machinery that is subject to evolution. In this paper, we explore the possibility that reaction systems of planetary atmospheres can serve as a null-model against which we can define metabolic structure and trace the influence of evolution. We find that the two types of data can be distinguished by their respective degree distributions. This is especially clear when looking at the degree distribution of the reaction network (of reaction connected to each other if they involve the same molecular species). For the Earth's atmospheric network and the human metabolic network, we look into more detail for an underlying explanation of this deviation. However, we cannot pinpoint a single cause of the difference, rather there are several concurrent factors. By examining quantities relating to the modular-functional organization of the metabolism, we confirm that metabolic networks have a more complex modular organization than the atmospheric networks, but not much more. We interpret the more variegated modular arrangement of metabolism as a trace of evolved functionality. On the other hand, it is quite remarkable how similar the structures of these two types of networks are, which emphasizes that the constraints from the chemical properties of the molecules has a larger influence in shaping the reaction system than does natural selection.
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| 2. |
- Holme, Petter, et al.
(författare)
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Subnetwork hierarchies of biochemical pathways
- 2003
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 19:4, s. 532-538
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: The vastness and complexity of the biochemical networks that have been mapped out by modern genomics calls for decomposition into subnetworks. Such networks can have inherent non-local features that require the global structure to be taken into account in the decomposition procedure. Furthermore, basic questions such as to what extent the network (graph theoretically) can be said to be built by distinct subnetworks are little studied. Results: We present a method to decompose biochemical networks into subnetworks based on the global geometry of the network. This method enables us to analyze the full hierarchical organization of biochemical networks and is applied to 43 organisms from the WIT database. Two types of biochemical networks are considered: metabolic networks and whole-cellular networks (also including for example information processes). Conceptual and quantitative ways of describing the hierarchical ordering are discussed. The general picture of the metabolic networks arising from our study is that of a few core-clusters centred around the most highly connected substances enclosed by other substances in outer shells, and a few other well-defined subnetworks.
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| 3. |
- Holme, Petter, et al.
(författare)
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Substance networks are optimal simple-graph representations of metabolism
- 2010
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Ingår i: Chinese Science Bulletin. - : Springer. - 1001-6538 .- 1861-9541. ; 55:27-28, s. 3161-3168
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Tidskriftsartikel (refereegranskat)abstract
- One approach to study the system-wide organization of biochemistry is to use statistical graph theory. In such heavily simplified methods, which disregard most of the dynamic aspects of biochemistry, one is faced with fundamental questions. One such question is how the chemical reaction systems should be reduced to a graph retaining as much functional information as possible from the original reaction system. In these graph representations, should the edges go between substrates and products, or substrates and substrates, or both? Should vertices represent substances or reactions? Different representations encode different information about the reaction system and affect network measures in different ways. This paper investigates which representation reflects the functional organization of the metabolic system in the best way, according to the defined criteria. Four different graph representations of metabolism (three where the vertices are metabolites, one where the vertices are reactions) are evaluated using data from different organisms and databases. The graph representations are evaluated by comparing the overlap between clusters (network modules) and annotated functions, and also by comparing the set of identified currency metabolites with those that other authors have identified using qualitative biological arguments. It is found that a “substance network”, where all metabolites participating in a reaction are connected, is better than others, evaluated with respect to both the functional overlap between modules and functions and to the number and identity of the identified currency metabolites.
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| 4. |
- Holme, Petter, et al.
(författare)
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Understanding and Exploiting Information Spreading and Integrating Technologies
- 2011
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Ingår i: Journal of Computer Science and Technology. - : Springer Science and Business Media LLC. - 1000-9000 .- 1860-4749. ; 26:5, s. 829-836
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Tidskriftsartikel (refereegranskat)abstract
- Our daily life leaves an increasing amount of digital traces, footprints that are improving our lives. Data-mining tools, like recommender systems, convert these traces to information for aiding decisions in an ever-increasing number of areas in our lives. The feedback loop from what we do, to the information this produces, to decisions what to do next, will likely be an increasingly important factor in human behavior on all levels from individuals to societies. In this essay, we review some effects of this feedback and discuss how to understand and exploit them beyond mapping them on more well-understood phenomena. We take examples from models of spreading phenomena in social media to argue that analogies can be deceptive, instead we need to fresh approaches to the new types of data, something we exemplify with promising applications in medicine.
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| 5. |
- Huss, Mikael, et al.
(författare)
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Currency and commodity metabolites : their identification and relation to the modularity of metabolic networks
- 2007
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Ingår i: IET Systems Biology. - : Institution of Engineering and Technology (IET). - 1751-8849 .- 1751-8857. ; 1:5, s. 280-285
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Tidskriftsartikel (refereegranskat)abstract
- The large-scale shape and function of metabolic networks are intriguing topics of systems biology. Such networks are on one hand commonly regarded as modular (i.e. built by a number of relatively independent subsystems), but on the other hand they are robust in a way not necessarily expected of a purely modular system. To address this question, we carefully discuss the partition of metabolic networks into subnetworks. The practice of preprocessing such networks by removing the most abundant substances, 'currency metabolites', is formalized into a network-based algorithm. We study partitions for metabolic networks of many organisms and find cores of currency metabolites and modular peripheries of what we call 'commodity metabolites'. The networks are found to be more modular than random networks but far from perfectly divisible into modules. We argue that cross-modular edges are the key for the robustness of metabolism.
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| 6. |
- Zhao, Jing, et al.
(författare)
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The Network Organization of Cancer-associated Protein Complexes in Human Tissues
- 2013
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- Differential gene expression profiles for detecting disease genes have been studied intensively in systems biology. However, it is known that various biological functions achieved by proteins follow from the ability of the protein to form complexes by physically binding to each other. In other words, the functional units are often protein complexes rather than individual proteins. Thus, we seek to replace the perspective of disease-related genes by disease-related complexes, exemplifying with data on 39 human solid tissue cancers and their original normal tissues. To obtain the differential abundance levels of protein complexes, we apply an optimization algorithm to genome-wide differential expression data. From the differential abundance of complexes, we extract tissue- and cancer-selective complexes, and investigate their relevance to cancer. The method is supported by a clustering tendency of bipartite cancer-complex relationships, as well as a more concrete and realistic approach to disease-related proteomics.
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