Metabolic Dissipative Structures - Systems Biology of Metabolic and Signaling Networks - page 197

(Lehman and Kelly 2002 ), Warburg effect (Levine and Puzio-Kuter 2010 ; Vasseur
et al. 2010 ), cell cycle (Verkest et al. 2005 ), epigenetic processes (Lim and van
Oudenaarden 2007 ), central carbon metabolism (Xu et al. 2008 ), DNA repair
(He et al. 2005 ), growth cell metabolism (Tennessen et al. 2011 ), and T-cell
activation and apoptosis (Perl et al. 2002 ).
In addition to the network topological structure, characterized by the specific
location of enzymatic subsystems, molecular substrate fluxes, and regulatory
signals, there is a global functional structure of biomolecular information flows
which is dynamic and able to modify the catalytic activities of all participating
enzymatic sets. Systemic metabolic structure not only involves a metabolic core
and self-organized multienzymatic complexes in an on-off mode, but also
determines a sophisticated structure of effective information flows which also
provides integrative coordination and synchronization between all metabolic
subsystems. The functional structure of biomolecular information flows is modular
and the dynamic changes between modules correspond to metabolic switches which
allow for critical transitions in the enzymatic activities. The modules of effective
connectivity and the functional switches seem to be important elements of the
systemic metabolic structure.
8.4 Concluding Remarks
In this work, we have addressed some aspects related with the self-organization of
metabolic processes in terms of information theory. Specifically, we have
quantified biomolecular information flows in bits between catalytic elements that
put in evidence essential aspects of metabolic function. In particular, we have
shown the emergence of effective connectivity structures and the functional coor-
dination between catalytic elements.
As a continuation of these results, in another work we have performed analyses
of different catalytic activities in a dissipative metabolic network based on statisti-
cal mechanics. We calculated the Shannon entropy and the energy function and
found that enzymatic activities are systemically governed by Hopfield-like
attractors with capacity to store functional catalytic patterns which can be correctly
recovered from specific input stimuli. The metabolic attractors regulate the catalytic
patterns, modify the efficiency in the connection between self-organized
multienzymatic complexes, and stably store these modifications (De la Fuente
et al. 2013 ). In the light of our results, the systemic metabolic structure appears to
operate as a complex information processing system which continuously defines
sets of biochemical instructions that make it to evolve into a particular and precise
catalytic regime for each multienzymatic subsystem (De la Fuente et al. 2013 ).
At present, we are working on the molecular mechanisms that link the metabolic
information emergent in the systemic metabolic structure with genetic information.
Understanding the principles and quantitative laws that govern the systemic self-
organization of enzymatic processes will be crucial to elucidate the structural and
functional architecture of cellular dynamics.