175
metabolism, the photosynthetic machinery performs important sensory functions
which further explain the interdependent regulation of pigment composition and
optical properties of eelgrass leaves by CO 2 , temperature, and light.
8.2.4 Integrating Transcriptomic Information into Predictive
Seagrass Models
As the above discussion indicates, mathematical modeling of ecophysiological processes can provide deep insights into complex systems. The application of systems
analysis to subcellular processes has been hindered by available technology and,
particularly in the case of seagrasses, the lack of a properly annotated genome on
which to base transcriptomic information. The study of seagrass biology, in particular, remains in the nascent stages of linking ecophysiology with genomic responses
(Procaccini et al. 2012). However, the explosion in “omics” technology during the
last two decades has opened a wealth of opportunities to exploit this information in
the development of genome-scale metabolic models that offer a “top-down” perspective on cellular function (Kim et al. 2012). The complete genome of Zostera
marina L. was recently published, revealing unique insights into the genomic losses
and gains involved in adapting to the marine environment (Olsen et al. 2016).
Transcriptomic analysis of Posidonia oceanica responses to different light environments revealed the upregulation of genes involved in photoacclimation and photoprotection by plants growing in shallow vs. deep water, suggesting shallow-growing
plants were experiencing stressful light conditions (Dattolo et al. 2014). Such
genome-scale (top-down) approaches are becoming extremely useful for identifying phenomenological correlations that can be mapped onto virtual subcellular
mechanisms that can serve as a scaffold for future experimental investigations
(Bruggeman and Westerhoff 2007).
In contrast, bottom-up approaches rely on explicitly defined mathematical relationships (e.g., photosynthesis vs. irradiance, Michaelis-Menten enzyme kinetics,
Arrhenius equation for temperature, etc.) that can be integrated to predict system
behavior (e.g., growth) in response to external drivers (e.g., light, temperature, CO 2 ,
and nutrient availability). As such, they rely heavily on experimentally determined
rate constants for accurate parameterization. The bio-optical seagrass model
GrassLight (Zimmerman 2003; Zimmerman et al. 2015) is an example of such a
bottom-up model. It currently possesses significant skill in predicting the lightlimited distribution of eelgrass in the Chesapeake region under present-day conditions and is consistent with the observations of dense, productive Posidonia oceanica
meadows growing in the warm, shallow, and volcanically acidified waters surrounding the Castello Aragonese on the island of Ischia, Bay of Naples, Italy (HallSpencer et al. 2008). As with eelgrass, the productivity and geographic distribution
of this species is typically inhibited by high summertime water temperature (Zupo
et al. 1997; Celebi et al. 2006).
8 Systems Biology and the Seagrass Paradox…
metabolism, the photosynthetic machinery performs important sensory functions
which further explain the interdependent regulation of pigment composition and
optical properties of eelgrass leaves by CO 2 , temperature, and light.
8.2.4 Integrating Transcriptomic Information into Predictive
Seagrass Models
As the above discussion indicates, mathematical modeling of ecophysiological processes can provide deep insights into complex systems. The application of systems
analysis to subcellular processes has been hindered by available technology and,
particularly in the case of seagrasses, the lack of a properly annotated genome on
which to base transcriptomic information. The study of seagrass biology, in particular, remains in the nascent stages of linking ecophysiology with genomic responses
(Procaccini et al. 2012). However, the explosion in “omics” technology during the
last two decades has opened a wealth of opportunities to exploit this information in
the development of genome-scale metabolic models that offer a “top-down” perspective on cellular function (Kim et al. 2012). The complete genome of Zostera
marina L. was recently published, revealing unique insights into the genomic losses
and gains involved in adapting to the marine environment (Olsen et al. 2016).
Transcriptomic analysis of Posidonia oceanica responses to different light environments revealed the upregulation of genes involved in photoacclimation and photoprotection by plants growing in shallow vs. deep water, suggesting shallow-growing
plants were experiencing stressful light conditions (Dattolo et al. 2014). Such
genome-scale (top-down) approaches are becoming extremely useful for identifying phenomenological correlations that can be mapped onto virtual subcellular
mechanisms that can serve as a scaffold for future experimental investigations
(Bruggeman and Westerhoff 2007).
In contrast, bottom-up approaches rely on explicitly defined mathematical relationships (e.g., photosynthesis vs. irradiance, Michaelis-Menten enzyme kinetics,
Arrhenius equation for temperature, etc.) that can be integrated to predict system
behavior (e.g., growth) in response to external drivers (e.g., light, temperature, CO 2 ,
and nutrient availability). As such, they rely heavily on experimentally determined
rate constants for accurate parameterization. The bio-optical seagrass model
GrassLight (Zimmerman 2003; Zimmerman et al. 2015) is an example of such a
bottom-up model. It currently possesses significant skill in predicting the lightlimited distribution of eelgrass in the Chesapeake region under present-day conditions and is consistent with the observations of dense, productive Posidonia oceanica
meadows growing in the warm, shallow, and volcanically acidified waters surrounding the Castello Aragonese on the island of Ischia, Bay of Naples, Italy (HallSpencer et al. 2008). As with eelgrass, the productivity and geographic distribution
of this species is typically inhibited by high summertime water temperature (Zupo
et al. 1997; Celebi et al. 2006).
8 Systems Biology and the Seagrass Paradox…