Network Dynamics in Cardiac Electrophysiology - Systems Biology of Metabolic and Signaling Networks - page 245

coupled network of random elements. In recent studies (Cui et al. 2009 ; Rovetti
et al. 2010 ; Nivala et al. 2011 , 2012a , b ), we started to develop models that integrate
random opening of ion channels and the spatial distribution of CRUs and
mitochondria to study the spatiotemporal dynamics. In future studies, models
integrating excitation, Ca 2+ cycling, signaling, metabolism, and contraction with
random ion channel opening are needed to reveal the emergent dynamics of cellular
electrophysiology.
10.4 Cellular Networks and Tissue-Scale Excitation
Dynamics
The heart is a network of different types of cells which are electronically coupled
via gap junctional conductance (Fig. 10.6a ). Electrical impulses originating from
the sino-atrial nodal region propagate to the atrium and the atrial-ventricular node,
and then the Purkinje network and the ventricles to cause synchronous contraction
of the heart. In normal ventricular tissue, a myocyte is coupled to about
11 myocytes, which is reduced to about six in ischemic tissue (Peters and Wit
1998 ). Recent studies have shown that fibroblasts may also be coupled to myocytes
(Camelliti et al. 2004a , b ).
In normal rhythm of the heart, the electrical impulse in heart is equivalent to a
planar wave (Fig. 10.6b ). Complex wave dynamics arise in cardiac tissue as results
of cellular dynamics and coupling between cells (Qu and Weiss 2006 ;Qu 2011 )to
result in cardiac arrhythmias, which includes focal excitations, spiral reentry, spiral
breakup (Fig. 10.6b ), and mixture of focal and reentry excitations (Sato et al. 2009 ).
These dynamics are emergent properties of the myocyte networks, which depend on
the cellular properties and how the cells are coupled.
10.5 Conclusions
In a network perspective, a physiological system (such as the heart) is a network
composed of subnetworks (Fig. 10.1 ). A biological function is an emergent prop-
erty of the coupled networks, not of a single gene or of a single protein. While
altering any of the elements may cause a change in biological functions, it is
necessary to understand how an element affects other elements in the network,
how properties emerge due to the interactions, and thus one can understand the
underlying mechanisms. Systems biology approaches that combine experimental
biology with computational biology are the likely solutions for dealing with such
complex problems. Moreover, computational modeling becomes even more and
more important since experimental tools are limited for revealing the complex
dynamics. Since the first cardiac action potential model developed by Noble in