Discrete-time L1 adaptive controller to regulate in vivo protein expressions

The application of DNA nanotechnology to interface with cellular environment provides tremendous opportunities to expand the synthetic biological circuits. The current application of DNA nanotechnology spans smart therapeutics (Douglas et al, Science 2012), drug delivery (Perrault, Shih, ACS Nano 2014), imaging (Choi et al, ACS Nano 2014), and probes for cell biology (Shaw et al, Nat Methods 2014). The excellent programmability of nucleic-acid-based parts would enlarge the space of complex functionalities realized in synthetic biological circuits. Building on our earlier works on DNA strand displacement circuits to regulate DNA tweezers driven by transcriptional oscillators, we show how discrete-time L1 adaptive controller can be used to deliver drugs in situ in response to cellular condition. For this, we replace the model predictive controller used (Menolascina et al, PLOS CB 2014). Our controller automatically regulates the administration of inducer molecules to the cells by comparing the actual protein expression level in the cell population with the desired expression level. We intend to use in the automated platform of (Menolascina et al, PLOS CB 2014) which is based on a microfluidic device, a time-lapse microscopy apparatus, and a set of motorized syringes, all controlled by a computer. They have tested the platform to force yeast cells to express a desired fixed, or time-varying, amount of a reporter protein over thousands of minutes. Here, the computer automatically switched the type of sugar administered to the cells, its concentration and its duration, according to the control algorithm. Our discrete-time L1 adaptive controller facilitates superior results on controlling expression of any protein, fused to a fluorescent reporter, provided that an external molecule known to (indirectly) affect its promoter activity is available. Conceptually, our controller is also compatible to work with optogenetic systems that allow one to generate desired perturbations in the intracellular concentration of a specific protein in microbial cell culture. As light can be easily added and removed, this enables an easier dynamic control of protein concentration in culture than would be possible with long-lived chemical inducers. Implementation of this closed-loop control scheme is achieved by sampling individual cells from the culture apparatus, imaging and quantifying protein concentration, and adjusting the inducing light appropriately. The culturing apparatus can be operated as a chemostat, allowing one to precisely control microbial growth and providing cell material for downstream assays. Apart from the obvious applications in phenotype regulations, this method of specifically perturbing the concentration of a single protein and measuring the downstream signaling and transcriptional responses will allow experimentalists to make more informative perturbations to better elucidate the kinetics and architecture of biological networks for disease diagnosis and drug delivery

Improved computation of natural logarithm using chemical reaction networks

Recent researches have focused on nucleic acids as a substrate for designing biomolecular circuits for in situ monitoring and control. A common approach is to express them by a set of idealised abstract chemical reaction networks (ACRNs). Here, we present new results on how abstract chemical reactions, viz., catalysis, annihilation and degradation, can be used to implement circuit that accurately computes logarithm function using the method of Cubic Arithmetic-Geometric Mean (AGM)

Load capacity improvements in transcriptional systems using discrete-time L1-adaptive control

DNA-based circuits relying on predictable thermodynamics and kinetics of DNA strand interactions impart flexibility in synthesizing synthetic biological constructs and in coupling these circuits to in vivo processes [1, 2, 6, 7]. Here, we focus on the synthetic Kim-Winfree oscillator network, illustrated in Fig. 1(i), which is a simple but effective coupled oscillator system in which two DNA switches SW1 and SW2 are coupled through activator and inhibitor blocks realized by RNA signals and auxiliary DNA species (see [3]). A typical experimental realization is closed in the sense that once the operation starts, we do not either add any chemicals, especially NTP fuel, externally into the wet-lab apparatus or remove any chemicals, especially waste products, from the apparatus. Within the closed system, the oscillations are bound to die out sooner or later diminishing NTP fuel eventually stops supporting the production of RNA signals and accumulating waste products clog down the toeholds and, as a result, adversely affect the signal propagation. Furthermore, the oxidation effects and the pH variations tend to deactivate the enzymes. Loading poses an additional challenge since it increases the order and the uncertainty of the system indeed, these oscillators have recently been used in [8] to drive conformational changes of a DNA nanomechanical device called DNA tweezers. We show how L1-adaptive control can be used to mitigate these effects

Implementing nonlinear feedback controllers using DNA strand displacement reactions

We show how an important class of nonlinear feedback controllers can be designed using idealized abstract chemical reactions and implemented via DNA strand displacement (DSD) reactions. Exploiting chemical reaction networks (CRNs) as a programming language for the design of complex circuits and networks, we show how a set of unimolecular and bimolecular reactions can be used to realize input-output dynamics that produce a nonlinear quasi sliding mode (QSM) feedback controller. The kinetics of the required chemical reactions can then be implemented as enzyme-free, enthalpy/entropy driven DNA reactions using a toehold mediated strand displacement mechanism via Watson-Crick base pairing and branch migration. We demonstrate that the closed loop response of the nonlinear QSM controller outperforms a traditional linear controller by facilitating much faster tracking response dynamics without introducing overshoots in the transient response. The resulting controller is highly modular and is less affected by retroactivity effects than standard linear designs

Integrated predictive genome-scale models to improve the metabolic re-engineering efficiency

One of the most common applications of metabolic circuits is to produce a desired chemical in a chassis organism, such as the Escherichia coli (E. coli), by importing heterologous genes encoding for the enzymes that participate in the biosynthetic pathway. Recently, an automated pipeline named RetroPath was developed to synthesise embedded metabolic circuits [1]. These circuits are to be embedded in E. coli for a wide range of applications such as regulating biomass productions, sensing specifc molecules, processing specific molecules, and releasing specific molecules. In this paper, we improve the efficiency of RetroPath via quadratic programming

Metabolic networks in a porcine model of trauma and hemorrhagic shock demonstrate different control mechanism with carbohydrate pre-feed

Background: Treatment with oral carbohydrate prior to trauma and hemorrhage confers a survival benefit in small animal models. The impact of fed states on survival in traumatically injured humans is unknown. This work uses regulatory networks to examine the effect of carbohydrate pre-feeding on metabolic response to polytrauma and hemorrhagic shock in a clinically-relevant large animal model. Methods: Male Yorkshire pigs were fasted overnight (n = 64). Pre-fed animals (n = 32) received an oral bolus of Karo\textregistered\syrup before sedation. All animals underwent a standardized trauma, hemorrhage, and resuscitation protocol. Serum samples were obtained at set timepoints. Proton NMR was used to identify and quantify serum metabolites. Metabolic regulatory networks were constructed from metabolite concentrations and rates of change in those concentrations to identify controlled nodes and controlling nodes of the network. Results: Oral carbohydrate pre-treatment was not associated with survival benefit. Six metabolites were identified as controlled nodes in both groups: adenosine, cytidine, glycerol, hypoxanthine, lactate, and uridine. Distinct groups of controlling nodes were associated with controlled nodes; however, the composition of these groups depended on feeding status. Conclusions: A common metabolic output, typically associated with injury and hypoxia, results from trauma and hemorrhagic shock. However, this output is directed by different metabolic inputs depending upon the feeding status of the subject. Nodes of the network that are related to mortality can potentially be manipulated for therapeutic effect; however, these nodes differ depending upon feeding status

Synthetic biology for engineering programmable soft materials [abstract]

Hydrogels based materials have drawn much attention within the past decade. Potentials for engineering their properties have broadened their applications from therapeutic devices to tough materials for use in robotics. These developments have coincided with the rise of synthetic biology that has led to new methods for artificial regulation of gene expression, engineering nano- and micro-scale modules within living systems, and interfacing living systems with inorganic materials. As such the past few years has seen the extensive integration of synthetic biology modules into hydrogels to impart sense and response functionalities. These novel biomaterials should be superior to traditional materials that suffer from 1) centralised, top-down and resource intensive manufacturing processes, and 2) a limited ability to sense and respond to their environments. Nonetheless hydrogels as the first generation of smart biomaterials still suffer from a limited signal bandwidth and sense/response abilities that are coupled with their bulk properties, where an output of the system is often reflected by a gel-sol phase transition. Recent efforts have tried to overcome these limitations with a view to creating materials with higher order intelligence and Boolean functionality, where the signal output is independent of the material properties. The fulfillment of this goal has been made possible due to the emergence of cell free transcription/translation, providing strategies for engineering structurally independent, programmable and orthogonal multi sense and response systems. Moreover, the convergent bottom-up approaches in the field of artificial life for the construction of minimal cells, is also expected to have unprecedented impact on smart material sciences in the years to come

Stability analysis of the GAL regulatory network in Saccharomyces cerevisiae and Kluyveromyces lactis

<p>Abstract</p> <p>Background</p> <p>In the yeast <it>Saccharomyces cerevisiae</it>, interactions between galactose, Gal3p, Gal80p, and Gal4p determine the transcriptional status of the genes required for the galactose utilization. Increase in the cellular galactose concentration causes the galactose molecules to bind onto Gal3p which, via Gal80p, activates Gal4p, which induces the GAL3 and GAL80 gene transcription. Recently, a linear time-invariant multi-input multi-output (MIMO) model of this GAL regulatory network has been proposed; the inputs being galactose and Gal4p, and the outputs being the active Gal4p and galactose utilization. Unfortunately, this model assumes the cell culture to be homogeneous, although it is not so in practice. We overcome this drawback by including more biochemical reactions, and derive a quadratic ordinary differential equation (ODE) based model.</p> <p>Results</p> <p>We show that the model, referred to above, does not exhibit bistability. We establish sufficiency conditions for the domain of attraction of an equilibrium point of our ODE model for the special case of full-state feedback controller. We observe that the GAL regulatory system of <it>Kluyveromyces lactis </it>exhibits an aberration of monotone nonlinearity and apply the Rantzer multipliers to establish a class of stabilizing controllers for this system.</p> <p>Conclusion</p> <p>Feedback in a GAL regulatory system can be used to enhance the cellular memory. We show that the system can be modeled as a quadratic nonlinear system for which the effect of feedback on the domain of attraction of the equilibrium point can be characterized using <it>linear matrix inequality </it>(LMI) conditions that are easily implementable in software. The benefit of this result is that a mathematically sound approach to the synthesis of full-state and partial-state feedback controllers to regulate the cellular memory is now possible, irrespective of the number of state-variables or parameters of interest.</p