The Control Analysis of Signal Transduction - Systems Biology of Metabolic and Signaling Networks - page 55

3.12 Perspectives
Molecular biological components and the systems in which they function
(e.g. substrates, enzymes, metabolites, genes in a cell, tissue, or organism) and
pathways mediating their functional outcome are many times more complex than
networks and circuits such as the London Underground. Yet, the London Under-
ground is already complex: making sure that one particular station functions effi-
ciently such that each minute one train could depart does not guarantee that indeed
one train will depart per minute. Most likely, many fewer trains will depart because
further down the tube there are other stations that are less efficient, or because at
some stations up the tube excessive numbers of people wish to board or leave the
train, or because the train driver overslept. Network studies reside at the crossroads
of disciplines, from mathematics (graph theory, combinatorics, probability theory)
to physics (statistical thermodynamics, macromolecular crowding), and from com-
puter science (network generating algorithms, combinatorial optimisation) to the
life sciences (metabolic and regulatory networks between proteins and nucleic
acids). The impact of network theory on understanding is strong in all natural
sciences (Barab ´ si and Albert 1999 ), especially in systems biology with gene
networks (Alon 2007 ), metabolic networks (Schuster et al. 2002 ), plant systems
biology, and even food webs (Getz et al. 2003 ). Yet, biological systems will not be
understood by existing network theory alone. Their properties are much more
complex than the properties of standard networks, for instance in that their networks
adapt and change temporarily, are hierarchical in terms of space, time, and
organisation, and have been optimised through evolution for multiple properties
that we do not yet understand. New network theories are needed and will have to be
more targeted towards understanding biological systems functionally. These will
have to integrate strongly with genomics and molecular data, because different
biological networks may need somewhat different theories, if only because their
objective (evolutionary purpose) is different.
Acknowledgement HVW, MV, and SR thank the transnational program Systems Biology of
MicroOrganisms (SysMO) and ERASysBioprogram and its funders EPSRC/BBSRC for
supporting the following projects (R111828, MOSES project, ERASysBio, BB/F003528/1,
BB/C008219/1, BB/F003544/1, BB/I004696/1, BB/I00470X/1, BB/I017186/1, EP/D508053/1,
BB/I017186/1). HVW also thanks other contributing funding sources including the NWO, and
EU-FP7 (EC-MOAN, UNICELLSYS, SYNPOL, ITFOM).
References
Alberghina L, Westerhoff HV (eds) (2005) Systems biology: definitions and perspectives.
Springer, Berlin
Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450-461
Bakker BM, Michels PAM, Opperdoes FR, Westerhoff HV (1999) What controls glycolysis in
bloodstream form Trypanosoma brucei? J Biol Chem 274:14551-9
Barab´si A-L, Albert R (1999) Emergence of scaling in random networks. Science 286:509-12