Costas Maranas

(1) Deciphering of plant metabolism: Reconstruction of metabolic models

Genome-scale models (GSMs) provide an effective means to study the biochemical transformations occurring in an organism by constructing comprehensive stoichiometric models of their metabolic networks. From the construction of the first GSM for the bacteria Haemophilus influenzae in 1995, they have been used to design and predict maximal yields of metabolic products and the associated optimal flux distributions. These computational methods are used to predict experimentally testable metabolic targets, thereby following an iterative design & test cycle. To this end, we are developing genome-scale metabolic models for poplar (Populus trichocarpa) and switchgrass (Panicum virgatum) in association with the Center for Bioenergy Innovation (CBI). Both plants are of interest as bioenergy crops.  In this project, we will provide students with a fundamental understanding of the steps involved in the bottom-up assembly of a plant metabolic network. This will include constructing an in silico stoichiometric model from genome annotation, curating gene-protein-reaction relationships, and designing organ-specific reaction networks using omics data so as to accurately inventory the organism’s metabolism. The extension of mathematical modeling to plant species is not without its challenges, mainly owing to the highly compartmentalized nature of metabolism. This division of labor apportions metabolic functions at the sub-cellular and organ levels, necessitating considerations such as the inclusion of accurate transport costs between compartments. The developed model will then be interrogated to identify key genes behind plant growth, yield, and composition. This will use strain-design algorithms developed in our lab such as OptKnock and OptForce and familiarize students with mixed-integer linear optimization methods. Ultimately, these designs will be passed to our experimental collaborators for validation.

We expect this project to provide an opportunity to learn the various tools involved in synthetic biology and mathematical optimization. This project will provide multidisciplinary training and education for one undergraduate student as a part of the REU program. The student will construct a whole-plant genome-scale model so as to obtain in silico predictions of cellular functions and leverage them to produce high-yielding feedstocks.

(2) Metabolic modeling of the microbiome

Microbes living as interacting communities are the most omnipresent life form on earth. These microbial communities, or microbiomes, can be found in various habitats, from oceans, soil to extreme environments including hot springs, volcanoes, acid mine drainage. Microbiomes also widely interact with other organisms such as plants, animals and humans. In the past decade, the question of ‘what is in there’ has been largely answered thanks to the advance of the next generation sequencing technologies, which enable the rapid culture-independent identification of microbial species in various environments. However, the important questions of what these microbes do and how they interact with each other remain elusive. Extending the maturing genome-scale metabolic (GSM) modeling approach to modeling the microbiome provides an emerging opportunity to capture and predict the interactions between the microbiome and the environment by integrating biochemical and ecological principles. To this end, we have developed a number of optimization frameworks for simulating the metabolism of microbial communities using GSM models, including OptCom, d-OptCom and SteadyCom. In this project, students will be familiarized with the biochemical knowledge, optimization modeling techniques as well as the programming procedure required for constructing community GSM models to simulate microbiome metabolism. We will apply the community simulation algorithms to a human gut microbiota model or a host-microbiota model for predicting intervention strategies (e.g., change in diet, administration of probiotic strains, genetic perturbations, etc.) that lead to an increasing production of anti-inflammatory molecules such as short-chain fatty acids (SCFAs). Alternatively, we will work on a case study to optimize the ethanol production by communities of organisms including Clostridium thermocellum, in association with the Center for Bioenergy Innovation (CBI). We will search for microbial partners for C. thermocellum that collectively make use of the C5 and C6 sugars from feedstock more efficiently for high-yield ethanol productions.

We expect this project to provide an in-depth understanding of modeling microbiome metabolism using genome-scale metabolic models for the purpose of biomedical or biotechnological applications. This project will provide multidisciplinary training and education for one undergraduate student as a part of the REU program. The student will perform simulations of microbial communities and identify in silico strategies that lead to desired biochemical productions by the communities.

(3) Metabolic pathway design, minimal organisms, and synthetic biology

This project is aimed at constructing a highly engineered, genetically reduced cyanobacterial strain to use as a platform for exploring the de novo CO2 assimilation pathways to improve photosynthesis yield and overall carbon fixation. Cyanobacteria as the ancient ancestor of the chloroplast provide a genetically tractable model for studying light capture and increasing photosynthetic carbon conversion efficiency, a necessity for meeting global agricultural needs. A reduced synthetic genome of cyanobacteria can help shed unwanted biological complexities and endogenous regulatory networks so as to retain and enhance the core functions needed for maximal carbon assimilation. We have recently developed a top-down genome minimization algorithm termed MinGenome, which will predict a ranked list of dispensable regions of an organism’s genome. We will work on applying the computational tool and collaborating with experimental labs using genome editing tools (e.g. CRISPR) to design a highly efficient cyanobacterial strain with a minimal genome at first for a phototrophic organism. In the second phase of the project, we will work on exploring new CO2 assimilation pathways for the minimal cyanobacteria. Although Calvin-Benson cycle is the dominant CO2 assimilation pathway in nature, the carbon fixation enzyme RuBisCO is less efficient and often has an energy-wasteful process of photorespiration. To this end, we have developed two stoichiometry-based computational frameworks termed optStoic and novoStoic to aid the design of new metabolic pathways for the production of target metabolites. We will work on applying the optimization-based computational algorithms to design de novo CO2 assimilation pathways for the genome-minimized cyanobacteria to replace Calvin-Benson cycle with more efficient carbon fixation enzymes.

We expect this project to provide a detailed understanding of metabolic pathway design and synthetic biology techniques for constructing a minimal genome. This project will provide multidisciplinary training and education for one undergraduate student as a part of the REU program. The student will perform simulations of computational tools and collaborate with experimental labs to design a minimal cyanobacterial strain with high carbon fixation rate.

Faculty Research Links

Contact Information

Esther Gomez, Ph.D.
Assistant Professor of Chemical Engineering
REU Program Coordinator

Manish Kumar, Ph.D.
Assistant Professor of Chemical Engineering
REU Program Coordinator

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