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Dietary fibre impacts the growth dynamics of human gut microbiota, yet we lack a detailed and quantitative understanding of how these nutrients shape microbial interaction networks and responses to perturbations. By building human gut communities coupled with computational modelling, we dissect the effects of fibres that vary in chemical complexity and each of their constituent sugars on community assembly and response to perturbations. We demonstrate that the degree of chemical complexity across different fibres limits microbial growth and the number of species that can utilize these nutrients. The prevalence of negative interspecies interactions is reduced in the presence of fibres compared with their constituent sugars. Carbohydrate chemical complexity enhances the reproducibility of community assembly and resistance of the community to invasion. We demonstrate that maximizing or minimizing carbohydrate competition between resident and invader species enhances resistance to invasion. In sum, the quantitative effects of carbohydrate chemical complexity on microbial interaction networks could be exploited to inform dietary and bacterial interventions to modulate community resistance to perturbations.

We show how subtraction can be performed via a simple chemical reaction network that includes molecular sequestration. The network computes the difference between the production rate parameters of the two mutually sequestering species. We benefit from introducing a simple change of variables, that facilitates the derivation of an approximate solution for the differential equations modeling the chemical reaction network, under a time scale separation assumption that is valid when the sequestration rate parameter is sufficiently fast. Our main result is that we provide simple expressions confirming that temporal subtraction occurs when the inputs are constant or time varying. Through simulations, we discuss two sequestration-based architectures for feedback control in light of the subtraction operations they perform.

Population heterogeneity can promote bacterial fitness in response to unpredictable environmental conditions. Human gut symbiontBacteroidesspp., displays variability in single cell surface architectures by combinatorial regulation of promoter inversions that drive expression of capsular polysaccharides (CPS). Using high-throughput single-cell sequencing, we reveal population heterogeneity generated through combinatorial promoter inversion and show thatB. fragilispopulations can access diverse CPS promoter states. Combining our data with stochastic computational modeling, we demonstrate that the rates of promoter inversion regulated by a broadly conserved serine recombinase are a major mechanism shaping population heterogeneity. Exploiting control over the expression of the recombinase, we devise a method for creating phase-locked variants and show that populations with different initial compositions converge to a similar steady-state composition over time. Our approach can be used to interrogate single-cell phase variable states of diverse microbes including bacterial pathogens.Summary sentenceHigh-throughput single cell sequencing of phase variation reveals regulation as a major driver of population diversification

Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.

Dietary fiber affects the composition and functions of microbial communities that reside in the human gut. However, we lack a detailed and quantitative understanding of how these nutrients shape microbial community dynamics, interaction networks and systems-level properties. Using synthetic human gut communities coupled to computational modeling, we dissect the effects of varied fiber types or their constituent sugars on community assembly and sensitivity to perturbations. By quantifying carbohydrate chemical complexity, we demonstrate that microbial growth decreases as a function of complexity. We further demonstrate that the balance of species occupying distinct metabolic niches is altered by the presence of chemically complex carbohydrates. The frequency of negative inter-species interactions is reduced in the presence of complex carbohydrates. Communities grown in complex carbohydrates reproducibly assemble from a wide range of initial species abundances and display reduced sensitivity to invasion. Resource competition is identified as a key mechanism influencing the response of communities to perturbations. The strength of resource competition can promote sensitivity of community assembly to variations in initial species proportions and impact community resistance to invasion. By limiting microbial growth, complex carbohydrates promote the expansion of species occupying niches beyond carbohydrate utilization, shape the distribution of inter-species interactions, which in turn determines the community’s response to perturbations.

Engineered genetic circuits with tailored functions that mimic how cells process information in changing environments (e.g. cell fate decision, chemotaxis, immune response) have great applications in biomedicine and synthetic biology. Although there is a lot of progress toward the design of gene circuits yielding desired steady states (e.g. logic-based networks), building synthetic circuits for dynamic signal processing (e.g. filters, frequency modulation, and controllers) is still challenging. Here, we provide a model-based approach to build gene networks that can operate as band-pass filters by taking advantage of molecular sequestration. By suitably approximating the dynamics of molecular sequestration, we analyze an Incoherent Feed-Forward Loop (IFFL) and a Negative Feedback (NF) circuit and illustrate how they can achieve band-pass filter behavior. Computational analysis shows that a circuit that incorporates both IFFL and NF motifs improves the filter performance. Our approach facilitates the design of sequestration-based filters, and may support the synthesis of molecular controllers with desired specifications.

Rewired and synthetic signaling networks can impart cells with new functionalities and enable efforts in engineering cell therapies and directing cell development. However, there is a need for tools to build synthetic signaling networks that are tunable, can precisely regulate target gene expression, and are robust to perturbations within the complex context of mammalian cells. Here, we use proteins derived from bacterial two-component signaling pathways to develop synthetic phosphorylation-based and feedback-controlled devices in mammalian cells with such properties. First, we isolate kinase and phosphatase proteins from the bifunctional histidine kinase EnvZ. We then use these proteins to engineer a synthetic covalent modification cycle, in which the kinase and phosphatase competitively regulate phosphorylation of the cognate response regulator OmpR, enabling analog tuning of OmpR-driven gene expression. Further, we show that the phosphorylation cycle can be extended by connecting phosphatase expression to small molecule and miRNA inputs in the cell, with the latter enabling cell-type specific signaling responses and accurate cell type classification. Finally, we implement a tunable negative feedback controller by co-expressing the kinase-driven output gene with the small molecule-tunable phosphatase. This negative feedback substantially reduces cell-to-cell noise in output expression and mitigates the effects of cell context perturbations due to off-target regulation and resource competition. Our work thus lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.

Understanding the principles of colonization resistance of the gut microbiome to the pathogen Clostridioides difficile will enable the design of defined bacterial therapeutics. We investigate the ecological principles of community resistance to C. difficile using a synthetic human gut microbiome. Using a dynamic computational model, we demonstrate that C. difficile receives the largest number and magnitude of incoming negative interactions. Our results show that C. difficile is in a unique class of species that display a strong negative dependence between growth and species richness. We identify molecular mechanisms of inhibition including acidification of the environment and competition over resources. We demonstrate that Clostridium hiranonis strongly inhibits C. difficile partially via resource competition. Increasing the initial density of C. difficile can increase its abundance in the assembled community, but community context determines the maximum achievable C. difficile abundance. Our work suggests that the C. difficile inhibitory potential of defined bacterial therapeutics can be optimized by designing communities featuring a combination of mechanisms including species richness, environment acidification, and resource competition., A combination of bottom‐up community assembly and computational modeling reveals determinants of Clostridioides difficile growth in synthetic human gut communities.

Understanding the principles of colonization resistance of the gut microbiome to the pathogen Clostridioides difficile will enable the design of next generation defined bacterial therapeutics. We investigate the ecological principles of community resistance to C. difficile invasion using a diverse synthetic human gut microbiome. Our results show that species richness is a key determinant of C. difficile growth across a wide range of ecological contexts. Using a dynamic computational model, we demonstrate that C. difficile receives the largest number and magnitude of incoming negative interactions. We identify molecular mechanisms of inhibition including acidification of the environment and competition over glucose. We demonstrate that C. difficile’s close relative Clostridium hiranonis strongly inhibits C. difficile via a pH-independent mechanism. While increasing the initial density of C. difficile can increase its abundance in the assembled community, the community context determines the maximum achievable C. difficile abundance. Our work suggests that the C. difficile inhibitory potential of defined bacterial therapeutics can be optimized by designing communities that feature a combination of mechanisms including species richness, environment acidification, and resource competition.

Background Although sustainable control since 1950s has achieved great successes, schistosomiasis japonica remains a major public health problem in China. Since 2004, a new integrated strategy was developed aiming to control the transmission of Schistosoma japonicum through the implementation of a package of interventions. To date, no systematic review or meta-analysis assessing the effectiveness of this new integrated strategy for schistosomiasis control in China has been published. We performed a PubMed-based bibliometric assessment of publications on the new integrated strategy for schistosomiasis japonica control in China, to understand the global transmissibility and sharing of the new integrated strategy. Methods An in-depth bibliometric analysis of all publications on the new integrated strategy for schistosomiasis japonica control in China was performed through a PubMed search using the terms “schistosomiasis” and “China,” from January 1, 2004 to August 31, 2018. All titles and abstracts were read carefully, and the publications reporting the effectiveness, experiences, lessons, or problems of the new integrated strategy were included in the bibliometric analysis. Results Overall, 2,361 titles were screened, and 70 eligible publications were accessed for analyses, including 23 studies in English, published in 15 international journals, and 47 studies in Chinese with abstracts in English, published in 3 national journals. Chinese Journal of Schistosomiasis Control (Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi) published 60% of the research output. Research articles (48.6%) and short reports (37.1%) were the dominant manuscript types. Furthermore, 471 contributing authors from 277 affiliations across 9 countries produced these 70 publications. Conclusion This is the first PubMed-based quantitative analysis of the research output of the new integrated strategy, and our data indicate a low global transmissibility of Chinese new integrated strategy. We therefore call for more research outputs of the new integrated strategy for schistosomiasis japonica control in China to be communicated through international platforms.

The behavior of genetic circuits is often poorly predictable. A gene’s expression level is not only determined by the intended regulators, but also affected by changes in ribosome availability imparted by expression of other genes. Here we design a quasi-integral 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 GOI’s translation and to actuate 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., Competition for shared cellular resources often renders genetic circuits poorly predictable. Here the authors design a biomolecular quasi-integral controller that allows gene expression to adapt to variable demand in translation resources.

A networked dynamical system is composed of subsystems interconnected through prescribed interactions. In many engineering applications, however, one subsystem can also affect others through "unintended" interactions that can significantly hamper the intended network's behavior. Although unintended interactions can be modeled as disturbance inputs to the subsystems, these disturbances depend on the network's states. As a consequence, a disturbance attenuation property of each isolated subsystem is, alone, insufficient to ensure that the network behavior is robust to unintended interactions. In this paper, we provide sufficient conditions on subsystem dynamics and interaction maps, such that the network's behavior is robust to unintended interactions. These conditions require that each subsystem attenuates constant external disturbances, is monotone or "near-monotone", the unintended interaction map is monotone, and the prescribed interaction map does not contain feedback loops. We employ this result to guide the design of resource-limited genetic circuits. More generally, our result provide conditions under which robustness of constituent subsystems is sufficient to guarantee robustness of the network to unintended interactions.

Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a device’s output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is loading of transcriptional and translational resources, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource loading in engineered mammalian genetic systems and develop an endoribonuclease-based feedforward controller that can adapt the expression level of a gene of interest to significant resource loading in mammalian cells. Near-perfect adaptation to resource loads is facilitated by high production and catalytic rates of the endoribonuclease. Our design is portable across cell lines and enables predictable tuning of controller function. Ultimately, our controller is a general-purpose device for predictable, robust, and context-independent control of gene expression., Accurately predicting the behaviour of a genetic circuit remains difficult due to the lack of modularity. Here the authors quantify the effects of resource loading in mammalian systems and develop an endoribonuclease-based feedfoward controller to adapt gene expression to the effects of resource loading.

With the explosive growth of network information, in order to obtain the information faster and more accurately, this paper proposes a text keyword extraction method based on Bert. Firstly, the key sentence set is extracted from the background material by Bert model as the information supplement to the text. Then, based on the extended text, TF-IDF, text rank and LDA are combined to extract keywords. The experimental results on real science and technology academic paper data sets show that the performance of the fusion multi type feature combination algorithm is better than that of the traditional single algorithm; and the F value of the algorithm is increased by 1.5% by extracting key sentences from background materials, which further improves the effect of key word extraction.

CRISPR-mediated gene regulation is known for its ability to control multiple targets simultaneously due to its modular nature: the same dCas9 effector can target different genes simply by changing the associated gRNA. However, multiplexing requires the sharing of limited amounts of dCas9 proteins among multiple gRNAs, leading to resource competition. In turn, competition between gRNAs for the same resource may hamper network function. In this work, we develop a general model that takes into account the sharing of a limited amount of dCas9 protein for arbitrary CRISPR-mediated gene repression networks. We demonstrate that, as a result of resource competition, hidden interactions appear, that modify the intended network regulations. As a case study, we analyze the effects of these hidden interactions in repression cascades. In particular, we illustrate that perfect adaptation to resource fluctuations can be achieved in cascades with an even number of repressors. In contrast, cascades with an odd number of repressors are substantially impacted by resource competition.

Recent trends in synthetic biology to move from prototypes to applications have triggered higher expectations on the robustness, predictability and responsiveness of biomolecular circuits. Therefore, a systematic approach to designing biomolecular controllers for regulating gene expression is needed. Although a number of integral control motifs (ICMs) have been proposed for set-point regulation, their performance in vivo is challenged by integration leakiness due to dilution, which cannot be neglected in growing cells. In this paper, we study a class of quasi-integral controllers designed based on existing ICMs and multiple time-scale separations. We demonstrate that by engineering all controller reactions to be much faster than dilution, set-point regulation can be achieved even in the presence of a leaky integrator. Furthermore, by engineering controller parameters for a second layer of time-scale separation, arbitrarily small tracking error can be achieved under certain technical conditions. We demonstrate a realization of our design principle through a small RNA feedback circuit., United States. Air Force. Office of Scientific Research (grant FA9550-14-1-0060), National Institutes of Health (U.S.). Civil, Mechanical and Manufacturing Innovation (award # 1727189)

Synthetic biology is the application of engineering principles to the fundamental components of biology, with the aim of creating systems with novel functionalities that can be used for energy, environment, and medical applications. While the potential impact of this new technology is enormous, there are challenges that we need to overcome before the impact of synthetic biology can be fully realized. Many of these challenges fall beyond the scope of molecular biology and are indeed “system-level” problems, where very little research is being performed. This paper identifies pressing challenges in synthetic biology that can be formulated as systems and control theoretic problems and outlines potentially new systems and control theories/tools that are required to tackle such problems. The aim is to attract more systems and control theorists to collaborate with molecular biologists and biophysicists and help synthetic biology reach its promise. At the same time, engaging the systems and control community more broadly into the rich research opportunities and life-changing applications of synthetic biology may provide added visibility to the field of systems and controls.

Supplemental information that contains additional technical details and simulation data.

Synthetic biology is an emergent research field whose aim is to engineer gene regulatory networks (GRNs) in living cells for useful functionalities. However, due to the unwanted interactions among nodes and with the cellular “chassis”, behavior of a GRN is often context dependent. One source of context dependence that has received much attention recently is the competition among nodes in a GRN for a limited amount of resources provided by the host cell. In this paper, we review our recent research outcomes on the modeling and mitigation of resource competition in GRNs from a control theoretic perspective. In particular, we demonstrate that resource competition gives rise to hidden interactions among nodes that change the intended network topology. By regarding hidden interactions as disturbances, we formulate a network disturbance decoupling problem that can be solved by implementing decentralized feedback controllers in the nodes. Our results provide examples of how introducing concepts in systems and control theory can lead to solutions to pressing practical problems in synthetic biology.

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., United States. Air Force. Office of Scientific Research (grant no. FA9550-14- 1-0060)

In a typical Real-time Hybrid Simulation (RTHS) setup, servo-hydraulic actuators serve as interfaces between the computational and physical substructures. Time delay introduced by actuator dynamics and complex interaction between the actuators and the specimen has detrimental effects on the stability and accuracy of RTHS. Therefore, a good understanding of servo-hydraulic actuator dynamics is a prerequisite for controller design and computational simulation of RTHS. This paper presents an easy-to-use parametric identification procedure for RTHS users to obtain re-useable actuator parameters for a range of payloads. The critical parameters in a linearized servo-hydraulic actuator model are optimally obtained from genetic algorithms (GA) based on experimental data collected from various specimen mass/stiffness combinations loaded to the target actuator. The actuator parameters demonstrate convincing convergence trend in GA. A key feature of this parametric modeling procedure is its re-usability under different testing scenarios, including different specimen mechanical properties and actuator inner-loop control gains. The models match well with experimental results. The benefit of the proposed parametric identification procedure has been demonstrated by (1) designing an H ∞ controller with the identified system parameters that significantly improves RTHS performance; and (2) establishing an analysis and computational simulation of a servo-hydraulic system that help researchers interpret system instability and improve design of experiments.

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 circuit’s behavior. Here, we create a library of genetic activation cascades in bacteriaE. coli, 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 non-regulatory interactions among genes that reshape circuit’s 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 (RBS) and DNA copy number to restore the cascade’s intended monotonically increasing response. Our results demonstrate how unintended circuit’s behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.

To artificially reprogram cell fate, experimentalists manipulate the gene regulatory networks (GRNs) that maintain a cell's 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 GRN's 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 GRN's 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. Keywords: feedback control; synthetic biology; cell fate; reprogramming; multistability; gene regulatory network, National Institute of General Medical Sciences (U.S.) (Grant P50 GMO98792)

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., United States. Air Force. Office of Scientific Research (grant FA9550-12-1-0129), National Institute of General Medical Sciences (U.S.) (grant P50 GMO98792)

In the past decade, researchers have been able to obtain pluripotent stem cells directly from an organism's differentiated cells through a process called cell reprogramming. This opens the way to potentially groundbreaking applications in regenerative and personalized medicine, in which ill patients could use self-derived induced pluripotent stem (iPS) cells where needed. While the process of reprogramming has been shown to be possible, its efficiency remains so low after almost ten years since its conception as to render its applicability limited to laboratory research. In this paper, we study a mathematical model of the core transcriptional circuitry among a set of key transcription factors, which is thought to determine the switch among pluripotent and early differentiated cell types. By employing standard tools from dynamical systems theory, we analyze the effects on the system's dynamics of overexpressing the core factors, which is what is performed during the reprogramming process. We demonstrate that the structure of the system is such that it can render the switch from an initial stable steady state (differentiated cell type) to the desired stable steady state (pluripotent cell type) highly unlikely. This finding provides insights into a possible reason for the low efficiency of current reprogramming approaches. We also suggest a strategy for improving the reprogramming process that employs simultaneous overexpression of one transcription factor along with enhanced degradation of another., Massachusetts Institute of Technology. Undergraduate Research Opportunities Program (Paul E. Gray Fund), United States. Air Force Office of Scientific Research (BRI Grant FA9550-14-1-0060)

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., United States. Air Force Office of Scientific Research (FA9550-12-1-0129), National Institute of General Medical Sciences (U.S.) (P50 GMO98792)

Hybrid powertrains with multiple sources of power have generated new control challenges in the automotive industry. Purdue University's 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 team's control development process for the EcoCAR 2 competition. It describes the team's 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 team's SIL and HIL simulations has also been presented.

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.

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., United States. Air Force. Office of Scientific Research (grant no. FA9550-14-1- 0060), United States. Office of Naval Research (grant no. N000141310074), National Science Foundation (U.S.). Graduate Research Fellowship Program

The recognition of prosodic structure is an important research aspect in the field of Text-to-Speech. It is essential to improving the naturalness of machine-synthesized speech. This paper proposes an approach to predicting and assigning prosodic structure automatically for Chinese sentences based on their tree structures. It presents the modeling of a statistical language model based on the simply handled corpus, the generation arithmetic of trees, and the method for predicting prosodic boundaries based on tree structures. The experimental results show that this tree-based method is effective, and it can obtain good prediction results.