215
Quantification of complex protein pools is also a challenge and is best conducted
using a pool of peptide standards matching the expected proteome. Exhaustive
species- specific prototypic peptides have made this possible for the human proteome (Kusebauch et al. 2016), but this does not immediately translate to other
species, and the theoretical pool of possible peptides in wild environments is prohibitively large for accurate and comprehensive de novo quantification. Nevertheless,
environmental proteomics have been able to directly identify certain functional proteins that present and operate in accord with environmental conditions (Saito et al.
2014), and the proteomic detection and quantification of specifically validated and
informative protein biomarkers may be an important tool for oceanography.
The broad measurement of metabolites and biomaterials in cells and natural systems similarly relies on adequate chemical separation, unique identification through
mass spectrometry, and validated standards for quantification and thus are similarly
challenging in terms of comprehensiveness and sensitivity compared to amplifiable
nucleic acids. Nevertheless, deep and informative biochemical datasets are emerging that can be integrated with transcriptomic, proteomic, and environmental data to
obtain cellular models that are predictive of basic emergent cellular properties.
10.6 Integration and Meta-analysis
The critical task and opportunity in systems biology and high-dimensional measurements (including omics) are to coherently integrate and apply these data into forms
that are easily distilled and amenable to testable scientific questions. Some of these
questions include
how do multiple experiments agree? Which aggregate patterns are robust and
unique? Which features are condition specific? What are the informative trends and
relationships between linked but orthogonal components: genome, transcriptome,
proteome, metabolome, phenotype, community, and environment? What is the
organizational and reactive information contained within the system, what are its
constraints, and how is it most likely transmitted?
In the case of transcriptome-wide gene expression, coherent microarray or RNA
sequencing data from many independent experiments can be readily integrated to
discover patterns of co-expression and conditionality that are only evident in aggregation. In the case of microalgae, as in other organisms, this can more powerfully
imply condition-specific units of implied co-regulation and function than individual
experiments alone (Hennon et al. 2015), partition all genes into subgroups of statistical and conditional relatedness within and between species (Ashworth et al. 2016),
and identify core features of apparent conditional metabolic control (Levering et al.
2017). The bioinformatics and integrative construction of metabolic models help to
organize, explain, and predict the flow of metabolites in new microalgal species
(Chang et al. 2011; Nogales et al. 2012; Levering et al. 2016; Kim et al. 2016).
Layering additional data types together into multi-scale models is also in some
sense simple, given adequately comprehensiveness and coherence (Karr et al. 2012).
10 Marine Microalgae: Systems Biology from ‘Omics’
Quantification of complex protein pools is also a challenge and is best conducted
using a pool of peptide standards matching the expected proteome. Exhaustive
species- specific prototypic peptides have made this possible for the human proteome (Kusebauch et al. 2016), but this does not immediately translate to other
species, and the theoretical pool of possible peptides in wild environments is prohibitively large for accurate and comprehensive de novo quantification. Nevertheless,
environmental proteomics have been able to directly identify certain functional proteins that present and operate in accord with environmental conditions (Saito et al.
2014), and the proteomic detection and quantification of specifically validated and
informative protein biomarkers may be an important tool for oceanography.
The broad measurement of metabolites and biomaterials in cells and natural systems similarly relies on adequate chemical separation, unique identification through
mass spectrometry, and validated standards for quantification and thus are similarly
challenging in terms of comprehensiveness and sensitivity compared to amplifiable
nucleic acids. Nevertheless, deep and informative biochemical datasets are emerging that can be integrated with transcriptomic, proteomic, and environmental data to
obtain cellular models that are predictive of basic emergent cellular properties.
10.6 Integration and Meta-analysis
The critical task and opportunity in systems biology and high-dimensional measurements (including omics) are to coherently integrate and apply these data into forms
that are easily distilled and amenable to testable scientific questions. Some of these
questions include
how do multiple experiments agree? Which aggregate patterns are robust and
unique? Which features are condition specific? What are the informative trends and
relationships between linked but orthogonal components: genome, transcriptome,
proteome, metabolome, phenotype, community, and environment? What is the
organizational and reactive information contained within the system, what are its
constraints, and how is it most likely transmitted?
In the case of transcriptome-wide gene expression, coherent microarray or RNA
sequencing data from many independent experiments can be readily integrated to
discover patterns of co-expression and conditionality that are only evident in aggregation. In the case of microalgae, as in other organisms, this can more powerfully
imply condition-specific units of implied co-regulation and function than individual
experiments alone (Hennon et al. 2015), partition all genes into subgroups of statistical and conditional relatedness within and between species (Ashworth et al. 2016),
and identify core features of apparent conditional metabolic control (Levering et al.
2017). The bioinformatics and integrative construction of metabolic models help to
organize, explain, and predict the flow of metabolites in new microalgal species
(Chang et al. 2011; Nogales et al. 2012; Levering et al. 2016; Kim et al. 2016).
Layering additional data types together into multi-scale models is also in some
sense simple, given adequately comprehensiveness and coherence (Karr et al. 2012).
10 Marine Microalgae: Systems Biology from ‘Omics’