Name
Abstract | ||
|---|---|---|
There has been a renewed interest for metabolism in the computational biology community, leading to an avalanche of papers coming from methodological network analysis as well as experimental and theoretical biology. This paper is meant to serve as an initial guide for both the biologists interested in formal approaches and the mathematicians or computer scientists wishing to inject more realism into their models. The paper is focused on the structural aspects of metabolism only. The literature is vast enough already, and the thread through it difficult to follow even for the more experienced worker in the field. We explain methods for acquiring data and reconstructing metabolic networks, and review the various models that have been used for their structural analysis. Several concepts such as modularity are introduced, as are the controversies that have beset the field these past few years, for instance, on whether metabolic networks are small-world or scale-free, and on which model better explains the evolution of metabolism. Clarifying the work that has been done also helps in identifying open questions and in proposing relevant future directions in the field, which we do along the paper and in the conclusion. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TCBB.2008.79 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
modeling,graph theory,scale free,structure analysis,metabolic network,structural analysis,data analysis,metabolism,computational biology,biochemistry,mathematical model,reconstruction,genetics,data modelling,network,modularity,network analysis,topology,chemical processes,evolution | Evolution biology,Mathematical and theoretical biology,Computer science,Bioinformatics,Network analysis,Realism,Modularity | Journal |
Volume | Issue | ISSN |
5 | 4 | 1545-5963 |
Citations | PageRank | References |
55 | 2.59 | 63 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincent Lacroix | 1 | 301 | 21.03 |
| Ludovic Cottret | 2 | 156 | 10.54 |
| Patricia Thébault | 3 | 79 | 6.40 |
| Marie-France Sagot | 4 | 1337 | 109.23 |