Search

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.

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.

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.

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.

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)

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)

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.

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.