R-3 Repository :: Browsing by Author "Ott, William"

Browsing by Author "Ott, William"

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Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia
(Public Library of Science, 2021) Winkle, James J.; Karamched, Bhargav R.; Bennett, Matthew R.; Ott, William; Josić, Krešimir
The increased complexity of synthetic microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases in metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics using cell-length control in monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strain’s division length, nematic cell alignment in close-packed monolayers can be destabilized. We find that this destabilization confers an emergent, competitive advantage to smaller-length strains—but by mechanisms that differ depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains, and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology.
Majority sensing in synthetic microbial consortia
(Springer Nature, 2020) Alnahhas, Razan N.; Sadeghpour, Mehdi; Chen, Ye; Frey, Alexis A.; Ott, William; Josić, Krešimir; Bennett, Matthew R.
As synthetic biocircuits become more complex, distributing computations within multi-strain microbial consortia becomes increasingly beneficial. However, designing distributed circuits that respond predictably to variation in consortium composition remains a challenge. Here we develop a two-strain gene circuit that senses and responds to which strain is in the majority. This involves a co-repressive system in which each strain produces a signaling molecule that signals the other strain to down-regulate production of its own, orthogonal signaling molecule. This co-repressive consortium links gene expression to ratio of the strains rather than population size. Further, we control the cross-over point for majority via external induction. We elucidate the mechanisms driving these dynamics by developing a mathematical model that captures consortia response as strain fractions and external induction are varied. These results show that simple gene circuits can be used within multicellular synthetic systems to sense and respond to the state of the population.
Methods for Predicting Synthetic Gene Circuits
(2020-02-25) Zong, David Mao; Bennett, Matthew; Ott, William
Mature engineering disciplines use computational tools to test designs before they are built, which allows rapid engineering design-build-test cycles. Synthetic biology is an immature engineering discipline because there is a dearth of computational tools that accurately predict how engineered systems behave. A strategy to improve computational methods is to build well-defined toy systems and study them using computational modeling. In this thesis, I built two such toy systems and studied their behavior using computational modeling. The first gene circuit I designed transcribes a gene of interest in response to multiple chemical signals. This design uses modular transcription factors to increase the number of possible chemical input combinations. However, this design is difficult to fully characterize because the number of possible input concentrations and input combinations are too numerous. We constructed a predictive model that accurately predicts gene expression for a set of input chemicals at any input concentration. The next system that I built is composed of three strains of engineered E. coli that interact using cell-cell signaling. This engineered population of bacteria pulses gene expression response to external signal. This pulse was modulated by changing the population fraction of each member species. We developed a computational model that predicts the behavior of the population in response to cell strain ratio. My work shows that complex synthetic biological systems can be tuned rationally and predictably using computational tools which makes engineering biology quicker.
Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations
(AIP Publishing, 2014) Gupta, Chinmaya; López, José Manuel; Azencott, Robert; Bennett, Matthew R.; Josić, Krešimir; Ott, William; Institute of Biosciences and Bioengineering
Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.
Modeling mechanical interactions in growing populations of rod-shaped bacteria
(IOP Publishing, 2017) Winkle, James J.; Igoshin, Oleg A.; Bennett, Matthew R.; Josić, Krešimir; Ott, William
Advances in synthetic biology allow us to engineer bacterial collectives with pre-specified characteristics. However, the behavior of these collectives is difficult to understand, as cellular growth and division as well as extra-cellular fluid flow lead to complex, changing arrangements of cells within the population. To rationally engineer and control the behavior of cell collectives we need theoretical and computational tools to understand their emergent spatiotemporal dynamics. Here, we present an agent-based model that allows growing cells to detect and respond to mechanical interactions. Crucially, our model couples the dynamics of cell growth to the cell's environment: Mechanical constraints can affect cellular growth rate and a cell may alter its behavior in response to these constraints. This coupling links the mechanical forces that influence cell growth and emergent behaviors in cell assemblies. We illustrate our approach by showing how mechanical interactions can impact the dynamics of bacterial collectives growing in microfluidic traps.
Timing and Variability of Galactose Metabolic Gene Activation Depend on the Rate of Environmental Change
(Public Library of Science, 2015) Nguyen-Huu, Truong D.; Gupta, Chinmaya; Ma, Bo; Ott, William; Josić, Krešimir; Bennett, Matthew R.; Institute of Biosciences and Bioengineering
Modulation of gene network activity allows cells to respond to changes in environmental conditions. For example, the galactose utilization network in Saccharomyces cerevisiae is activated by the presence of galactose but repressed by glucose. If both sugars are present, the yeast will first metabolize glucose, depleting it from the extracellular environment. Upon depletion of glucose, the genes encoding galactose metabolic proteins will activate. Here, we show that the rate at which glucose levels are depleted determines the timing and variability of galactose gene activation. Paradoxically, we find that Gal1p, an enzyme needed for galactose metabolism, accumulates more quickly if glucose is depleted slowly rather than taken away quickly. Furthermore, the variability of induction times in individual cells depends non-monotonically on the rate of glucose depletion and exhibits a minimum at intermediate depletion rates. Our mathematical modeling suggests that the dynamics of the metabolic transition from glucose to galactose are responsible for the variability in galactose gene activation. These findings demonstrate that environmental dynamics can determine the phenotypic outcome at both the single-cell and population levels.
Transcriptional Delay Stabilizes Bistable Gene Networks
(American Physical Society, 2013-08) Gupta, Chinmaya; López, José Manuel; Ott, William; Josić, Krešimir; Bennett, Matthew R.; Institute of Biosciences and Bioengineering
Transcriptional delay can significantly impact the dynamics of gene networks. Here we examine how such delay affects bistable systems. We investigate several stochastic models of bistable gene networks and find that increasing delay dramatically increases the mean residence times near stable states. To explain this, we introduce a non-Markovian, analytically tractable reduced model. The model shows that stabilization is the consequence of an increased number of failed transitions between stable states. Each of the bistable systems that we simulate behaves in this manner.
Tuning the dynamic range of bacterial promoters regulated by ligand-inducible transcription factors
(Springer Nature, 2018) Chen, Ye; Ho, Joanne M.L.; Shis, David L.; Gupta, Chinmaya; Long, James; Wagner, Daniel S.; Ott, William; Josić, Krešimir; Bennett, Matthew R.
One challenge for synthetic biologists is the predictable tuning of genetic circuit regulatory components to elicit desired outputs. Gene expression driven by ligand-inducible transcription factor systems must exhibit the correct ON and OFF characteristics: appropriate activation and leakiness in the presence and absence of inducer, respectively. However, the dynamic range of a promoter (i.e., absolute difference between ON and OFF states) is difficult to control. We report a method that tunes the dynamic range of ligand-inducible promoters to achieve desired ON and OFF characteristics. We build combinatorial sets of AraC-and LasR-regulated promoters containing -10 and -35 sites from synthetic and Escherichia coli promoters. Four sequence combinations with diverse dynamic ranges were chosen to build multi-input transcriptional logic gates regulated by two and three ligand-inducible transcription factors (LacI, TetR, AraC, XylS, RhlR, LasR, and LuxR). This work enables predictable control over the dynamic range of regulatory components.
Tuning the dynamic range of bacterial promoters regulated by ligand-inducible transcription factors
(Springer Nature, 2018) Chen, Ye; Ho, Joanne M.L.; Shis, David L.; Gupta, Chinmaya; Long, James; Wagner, Daniel S.; Ott, William; Josić, Krešimir; Bennett, Matthew R.
One challenge for synthetic biologists is the predictable tuning of genetic circuit regulatory components to elicit desired outputs. Gene expression driven by ligand-inducible transcription factor systems must exhibit the correct ON and OFF characteristics: appropriate activation and leakiness in the presence and absence of inducer, respectively. However, the dynamic range of a promoter (i.e., absolute difference between ON and OFF states) is difficult to control. We report a method that tunes the dynamic range of ligand-inducible promoters to achieve desired ON and OFF characteristics. We build combinatorial sets of AraC-and LasR-regulated promoters containing -10 and -35 sites from synthetic and Escherichia coli promoters. Four sequence combinations with diverse dynamic ranges were chosen to build multi-input transcriptional logic gates regulated by two and three ligand-inducible transcription factors (LacI, TetR, AraC, XylS, RhlR, LasR, and LuxR). This work enables predictable control over the dynamic range of regulatory components.

Browsing by Author "Ott, William"

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