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.

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.

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.

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)

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)

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