Triacylglycerols (TAGs) are highly reduced energy storage molecules ideal for biodiesel production. Microalgal TAG biosynthesis has been studied extensively in recent years, both at the molecular level and systems level through experimental studies and computational modeling. However, discussions of the strategies and products of the experimental and modeling approaches are rarely integrated and summarized together in a way that promotes collaboration among modelers and biologists in this field. In this review, we outline advances toward understanding the cellular and molecular factors regulating TAG biosynthesis in unicellular microalgae with an emphasis on recent studies on rate-limiting steps in fatty acid and TAG synthesis, while also highlighting new insights obtained from the integration of multi-omics datasets with mathematical models. Computational methodologies such as kinetic modeling, metabolic flux analysis, and new variants of flux balance analysis are explained in detail. We discuss how these methods have been used to simulate algae growth and lipid metabolism in response to changing culture conditions and how they have been used in conjunction with experimental validations. Since emerging evidence indicates that TAG synthesis in microalgae operates through coordinated crosstalk between multiple pathways in diverse subcellular destinations including the endoplasmic reticulum and plastids, we discuss new experimental studies and models that incorporate these findings for discovering key regulatory checkpoints. Finally, we describe tools for genetic manipulation of microalgae and their potential for future rational algal strain design. This comprehensive review explores the potential synergistic impact of pathway analysis, computational approaches, and molecular genetic manipulation strategies on improving TAG production in microalgae.

Kinetic modeling is increasingly used to understand the reaction dynamics of metabolic systems. However, one major drawback of kinetic modeling is that appropriate rate parameters required to implement such models are often unavailable. To circumvent this limitation, an approach known as structural kinetic modeling was developed as a way to understand the dynamics of reaction networks without explicitly requiring rate parameters. This study describes a novel approach to use structural kinetic modeling to identify reaction components that contribute most significantly to mediating network stability. We applied this method to analyze the metabolic pathway of glycolysis in yeast. As a result, we identified specific metabolic components that contribute most significantly to defining the stability properties of the glycolysis reaction network and predict the responses of these components to perturbations. These results were validated via comparison to a conventional kinetic model of glycolysis. Thus, applying our approach allows more detailed information about the stability and dynamics of the metabolic network to now be accessible without requiring rate parameters. We anticipate that this method can focus efforts of experimental studies by identifying the susceptibility of reaction components to metabolic engineering. The approach may be applied to a variety of complex reaction networks.

The extracellular matrix (ECM) is an assembly of proteins that surround cells, and serves as the cell substrate in vivo. A primary component of newly synthesized ECM is the fibronectin (FN). Despite many years of research, the mech-

Rapid advancements in biotechnology are expected to impact multiple areas of interest to the Army, including decontamination, degradation of toxic chemicals and biofuels. This project is a joint experimental/computational effort to map out the metabolic pathways in Clostridium acetobutylicum, and use this information todevelop a systems biology model of this system. This organism has been chosen specifically due to the fact that it has potential application to both biofuel production and nitroaromatic degradation. It is hoped that a systems biology model may provide key information to enhance both of these processes.