Toward Applications of Genomics and Metabolic Modeling to Improve Algal Biomass Productivity

Kourosh Salehi-Ashtiani, Joseph Koussa, Bushra Saeed Dohai, Amphun Chaiboonchoe, Hong Cai, Kelly A. D. Dougherty, David R. Nelson, Kenan Jijakli and Basel Khraiwesh

Abstract Genomic sequencing is the first step in a systems level study of an algal species, and sequencing studies have grown steadily in recent years. Completed sequences can be tied to algal phenotypes at a systems level through constructing genome-scale metabolic network models. Those models allow the prediction of algal phenotypes and genetic or metabolic modifications, and are constructed by tying the genes to reactions using enzyme databases, then representing those reactions in a concise mathematical form by means of stoichiometric matrices. This is followed by experimental validation using gene deletion or proteomics and metabolomics studies that may result in adding reactions to the model and filling phenotypic gaps. In this chapter, we offer a summary of completed and ongoing algal genomic projects before proceeding to holistically describing the process of constructing genome-scale metabolic models. Relevant examples of algal metabolic models are presented and discussed. The analysis of an alga’s emergent properties from metabolic models is also demonstrated using flux balance analysis (FBA) and related constraint-based approaches to optimize a given metabolic phenotype, or sets of phenotypes such as algal biomass. We also summarize readily available optimization tools rooted in constraint-based modeling that allow for optimizing bioproduction and algal strains. Examples include tools used to develop knockout strategies, identify optimal bioproduction strains, analyze gene deletions, and

Joseph Koussa and Bushra Saeed Dohai contributed equally to this work.

K. Salehi-Ashtiani (H) • J. Koussa • B. S. Dohai • A. Chaiboonchoe • H. Cai K. A.D. Dougherty • D. R. Nelson • B. Khraiwesh

Division of Science and Math, and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, United Arab Emirates e-mail: ksa3@nyu. edu

K. Jijakli

Division of Engineering, New York University Abu Dhabi, P. O. Box 129188,

Abu Dhabi, United Arab Emirates © Springer International Publishing Switzerland 2015

N. R. Moheimani et al. (eds.), Biomass and Biofuels from Microalgae,

Biofuel and Biorefinery Technologies 2, DOI 10.1007/978-3-319-16640-7_10

explore functional relationships within sets in a metabolic model. All in all, this systems level approach can lead to a better understanding and prediction of algal metabolism leading to more robust and cheaper applications.

10.1 Introduction

Algal research gained its first round of momentum, beyond the scientific commu­nity, in 1978 with the launch of the Aquatic Species Program to explore alternative transport fuel sources (Sheehan et al. 1998). As many laboratories started focusing their investigations entirely on algal systems, the amount of data available increased at a near exponential pace. In particular, the development of next-generation sequencing platforms has rapidly and dramatically advanced and increased the amount of available data on algal genomes. New high-throughput phenotypic platforms have made metabolic characterizations broader and more rapid. As in other fields of biological research, integration of disparate data types, as well as contextualization of data remains a central challenge that is addressed by the field of systems biology. Systems level understanding of metabolism is needed for pre­diction of biomass and bioproduct optimization strategies, for algae, or for any other organism because of the wide expanse and the high degree of interconnec­tivity in metabolic networks.

Evolutionarily, the term algae describe a polyphyletic group of organisms and is contentious in definite meaning (Proschold and Leliaert 2007). Currently, in some classifications, it includes superphyla in several separate lineages: stramenopiles which include brown, golden, and yellow algae and diatoms; rhodophyta or the red algae; photosynthetic alveolates, such as dinoflagelates; and the viridiplantae which include green algae (Barton et al. 2007). The phylogenetic classification green algae describe the presumed plant predecessors with photosynthetic capabilities, and a characteristic green color (Besche et al. 2009; Harris 2001). Both green algae and diatoms have shown great potentials as sustainable sources of biofuel, biomass, and bioproducts; however, Chlamydomonas reinhardtii, due to its position as a well studied representative green alga (Harris 2001), has received special interest as a model organism for genomic and metabolic studies.

The interest in algal exploration has steadily increased as commercial and large — scale production of lipid-producing algae has provided a practical importance for the research, with particular demand on more integrated goals, i. e., optimizing algae for biofuel production, optimizing growth of strains of interest and achieving economical viability all at the same time (Koussa et al. 2014). The metabolic optimization of algal organisms, which requires extensive characterization of algal metabolic circuitry, calls for an integrative “systems level” approach. This begins with genome sequencing, which then provides the “parts list” for reconstruction of a metabolic network, and finally ends with the ability to make model-based pre­dictions (Fig. 10.1). In this chapter, we first review microalgal sequencing efforts

investigations

Fig. 10.1 Schematic diagram representing the relationships between the reductionist, systems, and synthetic biology approaches. Through reductionism, a “top-down” approach leads to characterization of few or individual target genes. The collective knowledge gained through individual studies provides the framework for developing large-scale methodologies, building knowledgebases, and executing “bottom-up” omics studies, with the results integrated to describe the emergent properties of the system. Investigations of the models, constraints, and simulations provide predictions, which are implemented through an engineering approach, using biological parts or devices, for synthesis of a desired biological outcome that are in progress or have been completed, we then outline metabolic network reconstruction and constraint-based analyses, and to conclude, we briefly describe some computational tools that are used in metabolic modeling, which can aid the design of engineering experiments and optimization of cellular metabolic outputs.

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