Systems Level Regulation of Cardiac Energy Fluxes Via Metabolic Cycles: Role of Creatine, Phosphotransfer Pathways, and AMPK Signaling - Systems Biology of Metabolic and Signaling Networks - page 292

et al. 2009 ), and several more cell-type specific ion transporters, like epithelial Na +
channel (eNaC) (Carattino et al. 2005 ), renal Na + -K + -2Cl - cotransporter (NKCC2)
(Fraser et al. 2007 ), and neuronal voltage-gated, delayed rectifier K + channel
(Kv2.1), whose phosphorylation reduces the frequency of highly energy-consuming
action potentials (Ikematsu et al. 2011 ).
11.5.6 Modeling Signaling Networks in Heart and Beyond
Given the complexity and interconnectivity of cell signaling networks, only
recently emerging systems biology approaches hold promises for understanding
and predicting the higher order properties and behavior of such networks (Arkin
and Schaffer ( 2011 ) and following papers of this Cell Leading Edge Review series).
However, the mathematical modeling necessary for such systems approaches is still
in its infancy. Modeling needs a solid base of both quantitative and qualitative data,
including the spatiotemporal component as outlined above. So far, both bottom-up
and top-down systems approaches have been applied to obtain comprehensive
databases of protein kinase signaling. Typically, bottom-up, hypothesis-, or
model-driven approaches were used to study the role of individual components,
also facilitating first studies of dynamic systems properties. More recently, with the
broad availability of “omics” approaches, more top-down so-called hypothesis-free
studies have mapped interactomes and phosphoproteomes of protein kinases,
mainly in yeast (Breitkreutz et al. 2010 ; Bensimon et al. 2012 ; Oliveira
et al. 2012b ). Such large-scale data are necessary to construct first network models
needed to advance mathematical modeling in this field. Indeed, progress is also
made in the computational methodology and the mathematical description of
signaling pathways (Frey et al. 2008 ; Ideker et al. 2011 ; Telesco and Radhakrishnan
2012 ). However, similar modeling approaches with mammalian cells, in particular
under physiological and pathological conditions relevant to humans, are still scarce
(Benedict et al. 2011 ; Basak et al. 2012 ; Rogne and Tasken 2013 ), except a strong
history of modeling in cardiac electrophysiology (Amanfu and Saucerman 2011 ).
Such models would be highly valuable for in silico drug target identification, drug
screening, and development (Benedict et al. 2011 ). Important steps in such
approaches are (1) to establish a network structure, (2) to obtain quantitative
dynamic datasets for basic systems properties, (3) to generate dynamic mathemati-
cal models, and (4) to test and iteratively improve the models by prediction and
experimental verification of systems perturbations (Frey et al. 2008 ). First models
also including AMPK signaling are currently emerging (Marcus 2008 ; Sonntag
et al. 2012 ), but sustained interdisciplinary efforts in the field will be necessary to
obtain models that allow meaningful predictions of AMPK systems behavior.
Acknowledgments The authors would like to thank all group members involved in work cited in
this review. Work of the contributing groups was funded among others by EU FP6 and FP7
programs (contract LSHM-CT-2004-005272 EXGENESIS to U.S.; by reintegration grants