Metabolic Dissipative Structures - Systems Biology of Metabolic and Signaling Networks - page 191

enzyme catalyzes a reaction far from equilibrium and its regulation has been
considered an important instability-generating mechanism for the emergence of
oscillatory patterns in glycolysis (Goldbeter 2007 ). The TE studies give a quantifi-
cation of the effective connectivity and confirm that E 2 (phosphofructokinase) is the
key core of the pathway in this glycolytic model.
To summarize, the TE analysis shows the emergence of a new kind of dynamic
functional structure, characterized by changing connectivity flows that reflect
modulation of the kinetic behavior of the irreversible enzymes considered, and
catalytic coordination.

8.3 Self-Organized Catalytic Behavior in Dissipative
Metabolic Networks

Experimental observations (Almaas et al. 2004 , 2005 ) and numerical studies with
dissipative metabolic networks (De la Fuente et al. 1999a , 2008 ) have shown that
cellular enzymatic activity self-organizes. This spontaneous organization leads to
the emergence of a systemic metabolic structure characterized by a set of different
enzymatic processes locked into active states (metabolic core) while others present
on-off dynamics. Processes at the metabolic core and those exhibiting all-none
dynamics are fundamental traits of the systemic metabolic structure which may be
present in many different types of cells (Almaas et al. 2004 , 2005 ).
Recently, in an attempt to provide a more accurate understanding of the func-
tional coordination between several multienzymatic sets, the catalytic activities of a
dissipative metabolic network were studied using transfer esntropy (De la Fuente
et al. 2011 ). The results showed that a global functional structure of effective
connectivity emerges, which is dynamic and characterized by significant variations
of biomolecular information flows (De la Fuente et al. 2011 ).
In this study, a DMN of 18 metabolic subsystems, each one representing a set of
self-organized enzymes (MSb), was first performed. Figure 8.4 illustrates the
organization of substrate fluxes and substrate input fluxes of the DMN. Three
types of biochemical signals were considered in the network: activating (positive
allosteric modulation), inhibitory (negative allosteric modulation), and an all-or-
none type.
For building the DMN, several factors were chosen at random: (1) the number of
flux interactions, (2) the number of regulatory signals, (3) the parameters associated
with the flux-integration functions, (4) the regulatory coefficients of the allosteric
activities, and (4) the values of the initial conditions in the activities of all metabolic
subsystems (De la Fuente et al. 2011 ).
Since metabolic networks are open systems, we considered substrate input fluxes
from the environment. Here, MSb3 and MSb10 receive the constant substrate inputs
of S1

¼ 0.54 and S2 ¼ 0.16, arbitrarily fixed.