Curated Optogenetic Publication Database

Abstract: The field of synthetic biology focuses on creating modular components which can be used to generate complex and controllable synthetic biological systems. Unfortunately, the intrinsic noise of gene regulation can be large enough to break these systems. Noise is largely treated as a nuisance and much past effort has been spent to create robust components that are less influenced by noise. However, extensive analysis of noise combined with ‘smart’ microscopy tools and optognenetic actuators can create control opportunities that would be difficult or impossible to achieve in the deterministic setting. In previous work, we proposed an Optogenetic Maxwell’s Demons (OMD) control problem and found that deep understanding and manipulation of noise could create controllers that break symmetry between cells, even when those cells share the same optogenetic input and identical gene regulation circuitry. In this paper, we extend those results to analyze (in silico) the robustness of the OMD control under changes in system volume, with time observation/actuation delays, and subject to parametric model uncertainties.

Abstract: Optogenetics is a powerful technology to control synthetic gene circuits using external and computer-programmable light inputs. Like all biological processes, these systems are subject to intrinsic noise that arises from the stochastic process of gene regulation at the single-cell level. Many engineers have sought to mitigate this noise by developing more complex embedded bio-circuits, but recent work has shown that noise-exploiting stochastic controllers could enable new control strategies that take advantage of noise, rather than working against it. These noise-exploiting controllers were initially proposed to solve a single-input-multi-output stationary control problem, where symmetry was broken in a means reminiscent to the concept of Maxwell’s Demon. In this paper, we extend those results and show through computation that transient, asymmetric, and asynchronous stochastic control of the single-input-multi-output (SIMO) control problem is posible to achieve by cycling through different controllers in time. We show that such a method is able control two cells to two different periodic fates with different frequencies and different phases despite the use of only one control input.

Abstract: Synthetic biology seeks to develop modular biocircuits that combine to produce complex, controllable behaviors. These designs are often subject to noisy fluctuations and uncertainties, and most modern synthetic biology design processes have focused to create robust components to mitigate the noise of gene expression and reduce the heterogeneity of single-cell responses. However, a deeper understanding of noise can achieve control goals that would otherwise be impossible. We explore how an "Optogenetic Maxwell Demon" could selectively amplify noise to control multiple cells using single-input-multiple-output (SIMO) feedback. Using data-constrained stochastic model simulations and theory, we show how an appropriately selected stochastic SIMO controller can drive multiple different cells to different user-specified configurations irrespective of initial conditions. We explore how controllability depends on cells' regulatory structures, the amount of information available to the controller, and the accuracy of the model used. Our results suggest that gene regulation noise, when combined with optogenetic feedback and non-linear biochemical auto-regulation, can achieve synergy to enable precise control of complex stochastic processes.

Abstract: In recent years, the importance of mechanical signaling and the cellular mechanical microenvironment in affecting cellular behavior has been widely accepted. Cells in epithelial monolayers are mechanically connected to each other and the underlying extracellular matrix (ECM), forming a highly connected mechanical system subjected to various mechanical cues from their environment, such as the ECM stiffness. Changes in the ECM stiffness have been linked to many pathologies, including tumor formation. However, our understanding of how ECM stiffness and its heterogeneities affect the transduction of mechanical forces in epithelial monolayers is lacking. To investigate this, we used a combination of experimental and computational methods. The experiments were conducted using epithelial cells cultured on an elastic substrate and applying a mechanical stimulus by moving a single cell by micromanipulation. To replicate our experiments computationally and quantify the forces transduced in the epithelium, we developed a new model that described the mechanics of both the cells and the substrate. Our model further enabled the simulations with local stiffness heterogeneities. We found the substrate stiffness to distinctly affect the force transduction as well as the cellular movement and deformation following an external force. Also, we found that local changes in the stiffness can alter the cells’ response to external forces over long distances. Our results suggest that this long-range signaling of the substrate stiffness depends on the cells’ ability to resist deformation. Furthermore, we found that the cell’s elasticity in the apico-basal direction provides a level of detachment between the apical cell-cell junctions and the basal focal adhesions. Our simulation results show potential for increased ECM stiffness, e.g. due to a tumor, to modulate mechanical signaling between cells also outside the stiff region. Furthermore, the developed model provides a good platform for future studies on the interactions between epithelial monolayers and elastic substrates.

Abstract: Mathematical modeling has become a major tool to guide the characterization and synthetic construction of cellular processes. However, models typically lose their capacity to explain or predict experimental outcomes as soon as any, even minor, modification of the studied system or its operating conditions is implemented. This limits our capacity to fully comprehend the functioning of natural biological processes and is a major roadblock for the de novo design of complex synthetic circuits. Here, using a specifically constructed yeast optogenetic differentiation system as an example, we show that a simple deterministic model can explain system dynamics in given conditions but loses validity when modifications to the system are made. On the other hand, deploying theory from stochastic chemical kinetics and developing models of the system’s components that simultaneously track single-cell and population processes allows us to quantitatively predict emerging dynamics of the system without any adjustment of model parameters. We conclude that carefully characterizing the dynamics of cell-to-cell variability using appropriate modeling theory may allow one to unravel the complex interplay of stochastic single-cell and population processes and to predict the functionality of composed synthetic circuits in growing populations before the circuit is constructed.

Abstract: We report the first computational characterization of an optogenetic system composed of two photosensing BLUF (blue light sensor using flavin adenine dinucleotide) domains and two catalytic adenylyl cyclase (AC) domains. Conversion of adenosine triphosphate (ATP) to the reaction products, cyclic adenosine monophosphate (cAMP) and pyrophosphate (PPi), catalyzed by ACs initiated by excitation in photosensing domains has emerged in the focus of modern optogenetic applications because of the request in photoregulated enzymes that modulate cellular concentrations of signaling messengers. The photoactivated AC from the soil bacterium Beggiatoa sp. (bPAC) is an important model showing a considerable increase in the ATP to cAMP conversion rate in the catalytic domain after the illumination of the BLUF domain. The 1 μs classical molecular dynamics simulations reveal that the activation of the BLUF domain leading to tautomerization of Gln49 in the chromophore-binding pocket results in switching of the position of the side chain of Arg278 in the active site of AC. Allosteric signal transmission pathways between Gln49 from BLUF and Arg278 from AC were revealed by the dynamical network analysis. The Gibbs energy profiles of the ATP → cAMP + PPi reaction computed using QM(DFT(ωB97X-D3/6-31G**))/MM(CHARMM) molecular dynamics simulations for both Arg278 conformations in AC clarify the reaction mechanism. In the light-activated system, the corresponding arginine conformation stabilizes the pentacoordinated phosphorus of the α-phosphate group in the transition state, thus lowering the activation energy. Simulations of the bPAC system with the Tyr7Phe replacement in the BLUF demonstrate occurrence of both arginine conformations in an equal ratio, explaining the experimentally observed intermediate catalytic activity of the bPAC-Y7F variant as compared with the dark and light states of the wild-type bPAC.

Abstract: Optimization of engineered biological systems requires precise control over the rates and timing of gene expression. Optogenetics is used to dynamically control gene expression as an alternative to conventional chemical-based methods since it provides a more convenient interface between digital control software and microbial culture. Here, we describe the construction of a real-time optogenetics platform, which performs closed-loop control over the CcaR-CcaS two-plasmid system in Escherichia coli. We showed the first model-based design approach by constructing a nonlinear representation of the CcaR-CcaS system, tuned the model through open-loop experimentation to capture the experimental behavior, and applied the model in silico to inform the necessary changes to build a closed-loop optogenetic control system. Our system periodically induces and represses the CcaR-CcaS system while recording optical density and fluorescence using image processing techniques. We highlight the facile nature of constructing our system and how our model-based design approach will potentially be used to model other systems requiring closed-loop optogenetic control.

Abstract: Dynamic control of engineered microbes using light via optogenetics has been demonstrated as an effective strategy for improving the yield of biofuels, chemicals, and other products. An advantage of using light to manipulate microbial metabolism is the relative simplicity of interfacing biological and computer systems, thereby enabling in silico control of the microbe. Using this strategy for control and optimization of product yield requires an understanding of how the microbe responds in real-time to the light inputs. Toward this end, we present mechanistic models of a set of yeast optogenetic circuits. We show how these models can predict short- and long-time response to varying light inputs and how they are amenable to use with model predictive control (the industry standard among advanced control algorithms). These models reveal dynamics characterized by time-scale separation of different circuit components that affect the steady and transient levels of the protein under control of the circuit. Ultimately, this work will help enable real-time control and optimization tools for improving yield and consistency in the production of biofuels and chemicals using microbial fermentations.

Abstract: Synthetic biology has enabled the creation of biological reconfigurable circuits, which perform multiple functions monopolizing a single biological machine; Such a system can switch between different behaviours in response to environmental cues. Previous work has demonstrated switchable dynamical behaviour employing reconfigurable logic gate genetic networks. Here we describe a computational framework for reconfigurable circuits in E.coli using combinations of logic gates, and also propose the biological implementation. The proposed system is an oscillator that can exhibit tunability of frequency and amplitude of oscillations. Further, the frequency of operation can be changed optogenetically. Insilico analysis revealed that two-component light systems, in response to light within a frequency range, can be used for modulating the frequency of the oscillator or stopping the oscillations altogether. Computational modelling reveals that mixing two colonies of E.coli oscillating at different frequencies generates spatial beat patterns. Further, we show that these oscillations more robustly respond to input perturbations compared to the base oscillator, to which the proposed oscillator is a modification. Compared to the base oscillator, the proposed system shows faster synchronization in a colony of cells for a larger region of the parameter space. Additionally, the proposed oscillator also exhibits lesser synchronization error in the transient period after input perturbations. This provides a strong basis for the construction of synthetic reconfigurable circuits in bacteria and other organisms, which can be scaled up to perform functions in the field of time dependent drug delivery with tunable dosages, and sets the stage for further development of circuits with synchronized population level behaviour.