Yili Qian

Control theory meets synthetic biology

The past several years have witnessed an increased presence of control theoretic concepts in synthetic biology. This review presents an organized summary of how these control design concepts have been applied to tackle a variety of problems faced when building synthetic biomolecular circuits in living cells. In particular, we describe success stories that demonstrate how simple or more elaborate control design methods can be used to make the behaviour of synthetic genetic circuits within a single cell or across a cell population more reliable, predictable and robust to perturbations. The description especially highlights technical challenges that uniquely arise from the need to implement control designs within a new hardware setting, along with implemented or proposed solutions. Some engineering solutions employing complex feedback control schemes are also described, which, however, still require a deeper theoretical analysis of stability, performance and robustness properties. Overall, this paper should help synthetic biologists become familiar with feedback control concepts as they can be used in their application area. At the same time, it should provide some domain knowledge to control theorists who wish to enter the rising and exciting field of synthetic biology.

Resource Competition Shapes the Response of Genetic Circuits

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuits behavior. Here, we create a library of genetic activation cascades in E. coli bacteria, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to nonregulatory interactions among genes that reshape the circuits behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site and DNA copy number to restore the cascades intended monotonically increasing response. Our results demonstrate how unintended circuits behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.

Effective interaction graphs arising from resource limitations in gene networks

Protein production in gene networks relies on the availability of resources necessary for transcription and translation, which are found in cells in limited amounts. As various genes in a network compete for a common pool of resources, a hidden layer of interactions among genes arises. Such interactions are neglected by standard Hill-function-based models. In this work, we develop a model with the same dimension as standard Hill-function-based models to account for the sharing of limited amounts of RNA polymerase and ribosomes in gene networks. We provide effective interaction graphs to capture the hidden interactions and find that the additional interactions can dramatically change network behavior. In particular, we demonstrate that, as a result of resource limitations, a cascade of activators can behave like an effective repressor or a biphasic system, and that a repression cascade can become bistable.

A Blueprint for a Synthetic Genetic Feedback Controller to Reprogram Cell Fate

To artificially reprogram cell fate, experimentalists manipulate the gene regulatory networks (GRNs) that maintain a cells phenotype. In practice, reprogramming is often performed by constant overexpression of specific transcription factors (TFs). This process can be unreliable and inefficient. Here, we address this problem by introducing a new approach to reprogramming based on mathematical analysis. We demonstrate that reprogramming GRNs using constant overexpression may not succeed in general. Instead, we propose an alternative reprogramming strategy: a synthetic genetic feedback controller that dynamically steers the concentration of a GRNs key TFs to any desired value. The controller works by adjusting TF expression based on the discrepancy between desired and actual TF concentrations. Theory predicts that this reprogramming strategy is guaranteed to succeed, and its performance is independent of the GRNs structure and parameters, provided that feedback gain is sufficiently high. As a case study, we apply the controller to a model of induced pluripotency in stem cells.

Realizing ‘integral control’ in living cells: how to overcome leaky integration due to dilution?

A major problem in the design of synthetic genetic circuits is robustness to perturbations and uncertainty. Because of this, there have been significant efforts in recent years in finding approaches to implement integral control in genetic circuits. Integral controllers have the unique ability to make the output of a process adapt perfectly to disturbances. However, implementing an integral controller is challenging in living cells. This is because a key aspect of any integral controller is a ‘memory’ element that stores the accumulation (integral) of the error between the output and its desired set-point. The ability to realize such a memory element in living cells is fundamentally challenged by the fact that all biomolecules dilute as cells grow, resulting in a ‘leaky’ memory that gradually fades away. As a consequence, the adaptation property is lost. Here, we propose a general principle for designing integral controllers such that the performance is practically unaffected by dilution. In particular, we mathematically prove that if the reactions implementing the integral controller are all much faster than dilution, then the adaptation error due to integration leakiness becomes negligible. We exemplify this design principle with two synthetic genetic circuits aimed at reaching adaptation of gene expression to fluctuations in cellular resources. Our results provide concrete guidance on the biomolecular processes that are most appropriate for implementing integral controllers in living cells.

Mitigation of ribosome competition through distributed sRNA feedback

A current challenge in the robust engineering of synthetic gene networks is context dependence, the unintended interactions among genes and host factors. Ribosome competition is a specific form of context dependence, where all genes in the network compete for a limited pool of translational resources available for gene expression. Recently, theoretical and experimental studies have shown that ribosome competition creates a hidden layer of interactions among genes, which largely hinders our ability to predict design outcomes. In this work, we establish a control theoretic framework, where these hidden interactions become disturbance signals. We then propose a distributed feedback mechanism to achieve disturbance decoupling in the network. The feedback loop at each node consists of the protein product transcriptionally activating a small RNA (sRNA), which forms a translationally inactive complex with mRNA rapidly. We illustrate that with this feedback mechanism, protein production at each node is only dependent on its own transcription factor inputs, and almost independent of hidden interactions arising from ribosome competition.

Hybrid Electric Vehicle Powertrain Controller Development Using Hardware in the Loop Simulation

It is a time and cost consuming way to physically develop Hybrid Electric Vehicle (HEV) supervisor controller due to the increasing complexity of powertrain system. This study aims to investigate the HEV supervisor controller development process using dSPACE midsize Hardware in the Loop simulation system (HIL) for HEV powertrain control. The prototyping controller was developed on basis of MircoAutoBox II, and an HIL test bench was built on midsize HIL machine for the purpose of verification. The feasibility and capability of HIL were attested by the prototyping control strategy and fault modes simulation. The proposed approach was demonstrated its effectiveness and applicability to HEV supervisor controller development.

Development of a SIL, HIL and Vehicle Test-Bench for Model-Based Design and Validation of Hybrid Powertrain Control Strategies

Abstract Hybrid powertrains with multiple sources of power have generated new control challenges in the automotive industry. Purdue Universitys participation in EcoCAR 2, an Advanced Vehicle Technology Competition managed by the Argonne National Laboratories and sponsored by GM and DOE, has provided an exciting opportunity to create a comprehensive test-bench for the development and validation of advanced hybrid powertrain control strategies. As one of 15 competing university teams, the Purdue EcoMakers are re-engineering a donated 2013 Chevrolet Malibu into a plug-in parallel- through-the-road hybrid-electric vehicle, to reduce its environmental impact without compromising performance, safety or consumer acceptability.This paper describes the Purdue teams control development process for the EcoCAR 2 competition. It describes the teams efforts towards developing a complete vehicle model of a Parallel-through-the road PHEV which can leverage SIL and HIL simulation platforms for control development. A HIL test-bench was developed for real-time controller testing. The use of parameterized models, a prototyping controller and a unique interfacing philosophy allows the team to transition quickly between the SIL, HIL and vehicle platforms, thus providinga comprehensive test environment for the design and validation of various hybrid supervisory control strategies. Some preliminary data from the teams SIL and HIL simulations has also been presented.

Investigation of uncertainties associated with actuation modeling error and sensor noise on real time hybrid simulation performance

Real time hybrid simulation (RTHS) is a promising cyber-physical method for the experimental evaluation of civil engineering structures. RTHS allows for simulation of highly complicated civil engineering structures by partitioning them into numerical and physical (experimental) substructures, reducing the costs and time associated with a single test. Numerical and experimental RTHS substructures must be integrated with high fidelity at run-time. In recent years, a great deal of progress has been made to address the many challenges in conducting the physical portion of these simulations, such as hydraulic actuation and control, magneto-rheological (MR) dampers, and sensors, making RTHS a reality. However, systematic and random uncertainties developed in the physical/experimental substructure are inevitable and can have substantial impacts on the quality of the simulation results. Due to the interaction of the numerical and physical substructures in RTHS, uncertainties associated with the physical portion are amplified and degrade the quality of RTHS results. Compared to shake table testing, it has been shown that the reliability of hybrid simulation results is highly dependent upon how successfully experimental uncertainties are mitigated. Further studies are required to understand and quantify the impacts of various sources of physical uncertainties on the quality of the simulation results. In this paper, the impact of two inevitable uncertainties on the quality of the RTHS results is studied.

A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

The behavior of genetic circuits is often poorly predictable. A gene’s expression level is not only determined by the intended regulators, but also largely dictated by changes in ribosome availability imparted by activation or repression of other genes. To address this problem, we design a quasiintegral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOI’s mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOIs mRNA and to activate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits.