216
This is now also imminently possible for microalgae. Increasingly, deep data collection and performance-optimised “data-driven” statistics must be intimately applied
to justifiable biological hypotheses in order understand and compare profound and
complex features evident in single cells or entire ecosystems. Modeling ocean processes has evidently little do to with the tidy normal distributions and convenient
statistical models that easily obtained in the laboratory. In practice, assumptions
about what to expect in environmental datasets must be very carefully considered,
and designed uniquely to address new questions with statistically valid findings.
The computational challenge alone rivals the prodigious stargazing efforts and brilliant scientific advances of the national space programs of the 20th century. The
constellations of biological entities and processes occurring in the world’s oceans
still exceed our ability to fully observe them, or to accurately and predictively model
all of the biological processes operating in balance with changing environments.
This is a major new challenge for 21st century oceanography.
10.7 Prediction and Synthesis
The increasing depth and comprehensiveness available through high-throughput
molecular data collection now parameterizes the operational details of living systems in typically overwhelming detail. This detail is necessary but not sufficient to
constitute scientific knowledge and utility. The burden is on a [systems] biologist to
demonstrate the soundness, practicality, and relevance of their data, models, and
products to broader fields. How is high-data analysis and modeling useful, informative, and predictive? For what reasons were these complex data and models invested
in and what is their readily transferable scientific or technological value?
Two applicable aims in this regard are (1) predictions of present and future ecosystem properties and dynamics and (2) predictive testing, optimization, and reengineering of cellular and system-wide properties. The use of complex “whole-cell”
models with predictable aggregate properties could be used to deepen predictions of
genetic and cellular functions that drive ecosystems (Bragg et al. 2010), and
responses and adaptations of species and strains to ecological situations may soon
be modelable as a function of complex, interacting, and adaptable molecular programs whose inputs, rules, and outputs are predictable. Complementary to pure new
“data-driven” approaches are efficient and multiplexed laboratory experiments that
are able to directly probe the functions and impacts of specific new gene candidates
identified from conditional ‘omics experiments. Two exciting examples of this are
direct and thoroughly demonstrations of the evidently broad effects of transcription
factors and regulatory systems on productive microalgal phenotypes. The direct
experimental knockdowns of single transcription factors identified by targeted transcriptomics experiments in both P. tricornutum (Matthijs et al. 2017) and N. gaditana (Ajjawi et al. 2017) resulted in dramatic changes in metabolism and natural
product profiles, demonstrating the genetic and regulatory flexibility of microalgal
species with regard to microbial engineering.
J. Ashworth
This is now also imminently possible for microalgae. Increasingly, deep data collection and performance-optimised “data-driven” statistics must be intimately applied
to justifiable biological hypotheses in order understand and compare profound and
complex features evident in single cells or entire ecosystems. Modeling ocean processes has evidently little do to with the tidy normal distributions and convenient
statistical models that easily obtained in the laboratory. In practice, assumptions
about what to expect in environmental datasets must be very carefully considered,
and designed uniquely to address new questions with statistically valid findings.
The computational challenge alone rivals the prodigious stargazing efforts and brilliant scientific advances of the national space programs of the 20th century. The
constellations of biological entities and processes occurring in the world’s oceans
still exceed our ability to fully observe them, or to accurately and predictively model
all of the biological processes operating in balance with changing environments.
This is a major new challenge for 21st century oceanography.
10.7 Prediction and Synthesis
The increasing depth and comprehensiveness available through high-throughput
molecular data collection now parameterizes the operational details of living systems in typically overwhelming detail. This detail is necessary but not sufficient to
constitute scientific knowledge and utility. The burden is on a [systems] biologist to
demonstrate the soundness, practicality, and relevance of their data, models, and
products to broader fields. How is high-data analysis and modeling useful, informative, and predictive? For what reasons were these complex data and models invested
in and what is their readily transferable scientific or technological value?
Two applicable aims in this regard are (1) predictions of present and future ecosystem properties and dynamics and (2) predictive testing, optimization, and reengineering of cellular and system-wide properties. The use of complex “whole-cell”
models with predictable aggregate properties could be used to deepen predictions of
genetic and cellular functions that drive ecosystems (Bragg et al. 2010), and
responses and adaptations of species and strains to ecological situations may soon
be modelable as a function of complex, interacting, and adaptable molecular programs whose inputs, rules, and outputs are predictable. Complementary to pure new
“data-driven” approaches are efficient and multiplexed laboratory experiments that
are able to directly probe the functions and impacts of specific new gene candidates
identified from conditional ‘omics experiments. Two exciting examples of this are
direct and thoroughly demonstrations of the evidently broad effects of transcription
factors and regulatory systems on productive microalgal phenotypes. The direct
experimental knockdowns of single transcription factors identified by targeted transcriptomics experiments in both P. tricornutum (Matthijs et al. 2017) and N. gaditana (Ajjawi et al. 2017) resulted in dramatic changes in metabolism and natural
product profiles, demonstrating the genetic and regulatory flexibility of microalgal
species with regard to microbial engineering.
J. Ashworth