Dr. Lealon L Martin joins the Center for Computational Systems Biology to strengthen his research and further his collaboration with the center. Dr. Martin is a faculty member in the Chemical Engineering Department at Prairie View A&M University and also serves as Associate Dean (interim) of the Roy G. Perry College of Engineering. As an aside, Dr. Martin is also a licensed attorney and a member of the State Bar of Texas.

Dr. Martin, in collaboration with Dr. Seungchan Kim, was awarded a Sandia START collaborative grant (Jan 1, 2022 - Sept. 30, 2024) to study “Engineering Methanotrophy for Carbon Capture and Utilization”. The project is a collaboration with Sandia National Laboratory’s Bioresource & Environmental Security Team to identify key microbial and environmental factors for upregulating methanotrophy in typically methanogenic systems in agriculture and waste management, including landfills, waste-water, and rice cultivation, among others. In particular, Dr. Martin is looking to synthesis and reconstruction methane pathways in methanotrophic and methanogenic microorganisms, and provide guidance on how to maintain stable consortia of these microorganisms to convert greenhouse gases into biomass.

Project Title: Engineering Methanotrophy for Carbon Capture and Utilization

Principal Investigator: Seungchan Kim, Ph.D.

Purpose and Objective The purpose of the project is to identify key microbial and environmental factors for upregulating methanotrophy in typically methanogenic systems in agriculture and waste management, including landfills, waste-water, and rice cultivation, among others.

The Subcontractor shall use publicly available and Sandia National Laboratories (SNL) generated data to computationally identify interacting metabolic and microbial networks in methanogenic, methanotrophic, and perturbed consortia, for application in Agent Based Modelling (ABM) and metabolic Flux Balance Analysis (FBA).

Tasks

1. The Subcontractor shall provide the SNL Principal Investigator (PI) with preliminary analysis, based on literature review, for determination of experimental data requirements for statistically significant biochemical and microbial network analysis results from a Design-Build-Test-Learn (DBTL) cycle incorporating Design of Experiments (DoE) based on methanogenic and methanotrophic microbial consortia.

2. The Subcontractor shall apply network analysis using SNL provided multi-omics data, including microbiomes from 16S rRNA-amplicon sequencing, phenotypic analysis, and flux of major metabolytes from testbed cultures. Major microbial and biochemical nodes that are identified shall provide the SNL R&D team insights into engineering targets for the intended applications.

3. The Subcontractor shall deliver reduced-order cohort model scenarios composed of 3-10 agents from all available data for implementation in ABM. The cohort model scenarios shall include key interacting microbial species and associated biochemical pathways relevant to maximizing and stabilizing methanotrophy in the presence of methanogenic agents, and include sensitivity estimates and environmental bounds (temperature, pH, oxygen, redox potential, conductivity, nutrients, and gross substrate flux balance) for each cohort.