(466f) EuGeneCD: A Computational Tool for Qualitative Genetic Circuit Design for Eukaryotic Organisms
AIChE Annual Meeting
2019
2019 AIChE Annual Meeting
Food, Pharmaceutical & Bioengineering Division
Mammalian Synthetic Biology
Wednesday, November 13, 2019 - 9:30am to 9:48am
EuGeneCD: A computational tool for qualitative genetic circuit
design for eukaryotic organisms Wheaton Schroeder and Rajib Saha The University of Nebraska – Lincoln, Lincoln, NE Synthetic
genetic circuits, first created about two decades ago, are tools which allow
biological systems to perform dynamic or binary computations often with respect
to some external signal allowing for more precise control of redesigned
biological systems. These tools are proving increasingly useful in the rational
redesigning of biological processes directed to the overproduction of a
specific bioproduct, the engineering of a desired behavior, or improvement of a
biological systems performance. However, implementing genetic circuits has been
proven to be challenging, and therefore expensive, to rationally or intuitively
create, screen cultures, and measure behavior in vivo. Therefore, in silico
computational modeling of biological processes, generally metabolism, is often being used to drive
the understanding and designing of genetic circuits. While some recent
computational tools and models have been created to aid in this process, many
are in silico aids to the design of
specific bioparts, such as ribosome binding sites or
transcription terminators, and as such cannot be used for holistic in silico circuit design, although some
tools for in silico holistic design
do exist. Furthermore, the majority (if not all) of these tools are for
prokaryotic systems, which have significant and important differences in both
transcription and translation compared to eukaryotic systems. Here, we
introduce a mixed integer linear programming (MILP) tool EuGeneCD (Eukaryotic Genetic Circuit Design tool), a computational tool which uses inputs of potential
circuit bioparts, qualitative part information, and
desired binary computation behavior to design a genetic circuit with
course-grained information on circuit behavior in response to an input set of
ligands through an optimization-based approach. Zea
mays
(maize), as an important agricultural species which may benefit from genetic
circuits, was chosen to verify the utility of EuGeneCD
through two in silico case studies.
The first case study involved the use of fluorescent proteins (blue, red,
green, and yellow) as reporters in response to the available carbon source(s) (combinations
of glucose and L-arabinose) in the maize root environment. Several possible
genetic circuit designs were proposed by EuGeneCD which
utilize different combinations of bioparts and
different binary logical structures to perform the desired binary computation,
dominated by internal and, nor, and not logical gates. The resulting circuit
design differs in important ways from equivalent circuit designs for E. coli, highlighting the need for a
eukaryotic tool. The second in silico
case study involves the designing of a genetic circuit for controlling how rhizobiome interacts with maize root through the regulation
of classes of signaling molecule, which has been shown to be responsible for
regulating plant-rhizobiome interactions. In this
case, by controlling strigolactone and flavonoids exudates
through the use of a genetic circuit, the
environmental conditions in and around the maize root may be tuned to favor mycelium
or bacterium which meet current plant needs. Several possible circuit designs
for effecting the desired environmental changes are proposed by EuGeneCD.
design for eukaryotic organisms Wheaton Schroeder and Rajib Saha The University of Nebraska – Lincoln, Lincoln, NE Synthetic
genetic circuits, first created about two decades ago, are tools which allow
biological systems to perform dynamic or binary computations often with respect
to some external signal allowing for more precise control of redesigned
biological systems. These tools are proving increasingly useful in the rational
redesigning of biological processes directed to the overproduction of a
specific bioproduct, the engineering of a desired behavior, or improvement of a
biological systems performance. However, implementing genetic circuits has been
proven to be challenging, and therefore expensive, to rationally or intuitively
create, screen cultures, and measure behavior in vivo. Therefore, in silico
computational modeling of biological processes, generally metabolism, is often being used to drive
the understanding and designing of genetic circuits. While some recent
computational tools and models have been created to aid in this process, many
are in silico aids to the design of
specific bioparts, such as ribosome binding sites or
transcription terminators, and as such cannot be used for holistic in silico circuit design, although some
tools for in silico holistic design
do exist. Furthermore, the majority (if not all) of these tools are for
prokaryotic systems, which have significant and important differences in both
transcription and translation compared to eukaryotic systems. Here, we
introduce a mixed integer linear programming (MILP) tool EuGeneCD (Eukaryotic Genetic Circuit Design tool), a computational tool which uses inputs of potential
circuit bioparts, qualitative part information, and
desired binary computation behavior to design a genetic circuit with
course-grained information on circuit behavior in response to an input set of
ligands through an optimization-based approach. Zea
mays
(maize), as an important agricultural species which may benefit from genetic
circuits, was chosen to verify the utility of EuGeneCD
through two in silico case studies.
The first case study involved the use of fluorescent proteins (blue, red,
green, and yellow) as reporters in response to the available carbon source(s) (combinations
of glucose and L-arabinose) in the maize root environment. Several possible
genetic circuit designs were proposed by EuGeneCD which
utilize different combinations of bioparts and
different binary logical structures to perform the desired binary computation,
dominated by internal and, nor, and not logical gates. The resulting circuit
design differs in important ways from equivalent circuit designs for E. coli, highlighting the need for a
eukaryotic tool. The second in silico
case study involves the designing of a genetic circuit for controlling how rhizobiome interacts with maize root through the regulation
of classes of signaling molecule, which has been shown to be responsible for
regulating plant-rhizobiome interactions. In this
case, by controlling strigolactone and flavonoids exudates
through the use of a genetic circuit, the
environmental conditions in and around the maize root may be tuned to favor mycelium
or bacterium which meet current plant needs. Several possible circuit designs
for effecting the desired environmental changes are proposed by EuGeneCD.