(752a) A Dynamic Genome-Scale Metabolic Network Model for a Novel Methanotroph-Cyanobacteria Coculture

Authors: 
Badr, K., Auburn University
He, Q. P., Auburn University
Wang, J., Auburn University

Kiumars Badr Normal Kiumars Badr 2 37 2019-04-12T13:46:00Z 2019-04-13T00:58:00Z 2019-04-13T00:58:00Z 2 747 4261 35 9 4999 16.00 american-chemical-society

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1.1pt"> " times new roman major-bidi>A Dynamic Genome-scale Metabolic
Network Model for a Novel Methanotroph-Cyanobacteria Coculture

Kiumars Badr, Q.
Peter He and Jin Wang*

Department of
Chemical Engineering, Auburn University, Auburn, AL, 36849, USA.       *Email: wang@auburn.edu

Biogas
is comprised primarily of methane (50%~70%) and carbon dioxide (30% ~50%),
which can be produced from various waste sources, including landfill material,
animal manure; wastewater; and industrial, institutional, and commercial
organic wastes. EPA estimates that currently US biogas production potential is
654 billion cubic feet per year, which could displace 7.5 billion gallon of gasoline [1]. 
It is clear that biogas has immense potential
as a renewable feedstock for producing high-density fuels and commodity
chemicals. However, the utilization of
biogas represents a significant challenge due to its low pressure and presence
of contaminants such as H2S, ammonia, and volatile organic carbon
compounds. To tap into this immense potential, effective biotechnologies that
can co-utilize both CO2 and CH4 are needed.

In
our previous work, we have demonstrated that metabolic bold">coupling of methane oxidation to oxygenic photosynthesis can be a highly
efficient way to recover the energy and capture carbon from biogas [1, 2]. However,
development of multi-organism platforms for commercial biogas conversion
present significant challenges which center around our ability to control
function and composition of species in the Coculture. An essential tool for the
optimization, design and analysis of the coculture based biogas conversion is
the development and validation of kinetics models that can accurately describe
and predict the co-culture growth under different conditions [3]. To this end,
using Methylomicrobium " times new roman> buryatense - Arthrosipira platensis as the model coculture
system, we have developed an unstructured
model to capture the growth dynamics. Specifically, Monod-like models were
developed to capture coculture growth. Two sources of substrate were considered
in the model: gas transferred from gas phase and gas produced in situ
In addition, we rely on the fitted maximum cell growth rate for both
strains to capture other potential interactions. Using designed experiments and
the developed model, we clearly demonstrated that the synergistic effect within
the coculture cannot be fully explained by the in situ
substrate exchange, and there must be other “metabolic links” to explain the
significantly enhanced cell growth of both strains in the coculture.  

In
order to
test different hypothesized “metabolic links” between the Methanotroph and
cyanobacteria in the Coculture, in the work, we developed a structured, dynamic
genome-scale metabolic network model for the Coculture system. These GEMs,
especially the dynamic GEMs, offer a comprehensive picture of cellular
metabolism and serve as a bridge that can better link the work of
microbiologists and engineers in understanding and optimizing complex cellular
metabolism. By integrating the available knowledge on each strain with data
obtained in our own experiments, we use DFBAlab to
implement the dynamic GEM for the Coculture [4]. For the dynamic GEM, besides
the GEMs for each individual strain, the key inputs to the DFBA model are the uptake
kinetics for different substrates. In this work, the substrate update rates are
provided from the unstructured dynamic model we already developed. In addition,
the product secretion rates and cell growth rates predicted by the validated
unstructured model were used to validate the coculture dynamic GEM. Although
the experimental measurements to validate the model are mainly limited to cell
growth, substrate consumption and product secretion rates for each organism in
the coculture, the overall dynamic trajectory (both measured low-frequency
samples and unstructured model predicted high-frequency samples) offers
significantly more power in validating the model, compared to a few steady
state conditions. The validated coculture GEM enables the testing of different
hypotheses on potential “metabolic links” within the coculture.

Reference:

[1] Badr, K.,
Hilliard, M., He, Q.P., & Wang, J., “Understanding the Stability and
Robustness of a Methanotroph-Cyanobacterium Coculture through Kinetic Modeling
and Experimental Validation.” Annual AIChE Meeting.
Pittsburgh, PA (2018).

[2] Badr K,
Roberts N, Hilliard M, He QP, Wang J. “Photoautotroph-Methanotroph Coculture –
A Flexible Platform for Efficient Biological CO2 – CH4 Co-utilization,”
Dynamics and Control of Process Systems, including Biosystems - 12th DYCOPS. (2019);
accepted.

[3] Almquist J., M. Cvijovic, V. Hatzimanikatis, J. Nielsen, M. Jirstrand, “Kinetic models in industrial biotechnology –
improving cell factory performance”, Metab.
Eng., 24 (2014), pp. 38-60.

[4] Gomez
JA, Höffner K, Barton PI. DFBAlab:
a fast and reliable MATLAB code for dynamic flux balance analysis. BMC
Bioinformatics. (2014),15:409.