How to turn a genetic circuit into a synthetic tunable oscillator, or a bistable switch

Control-Based Continuation: A New Approach to Prototype Synthetic Gene Networks

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modeling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch, which includes additive process noise and exhibits bistability. We compare the results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of an in vivo experiment using an agent-based simulator describing cell growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

Optimal Robust Search for Parameter Values of Qualitative Models of Gene Regulatory Networks

Computational and mathematical models are a must for the in silico analysis or design of Gene Regulatory Networks (GRN)as they offer a theoretical context to deeply address biological regulation. We have proposed a framework where models of network dynamics are expressed through a class of nonlinear and temporal multiscale Ordinary Differential Equations (ODE). To find out models that disclose network structures underlying an observed or desired network behavior, and parameter values that enable the candidate models to reproduce such behavior, we follow a reasoning cycle that alternates procedures for model selection and parameter refinement. Plausible network models are first selected via qualitative simulation, and next their parameters are given quantitative values such that the ODE model solution reproduces the specified behavior. This paper gives algorithms to tackle the parameter refinement problem formulated as an optimization problem. We search, within the parameter space symbolically expressed, for the largest hypersphere whose points correspond to parameter values such that the ODE solution gives an instance of the given qualitative trajectory. Our approach overcomes the limitation of a previously proposed stochastic approach, namely computational load and very reduced scalability. Its applicability and effectiveness are demonstrated through two benchmark synthetic networks with different complexity.

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli-based transcriptional event detector.

Control of tipping points in stochastic mutualistic complex networks

Nonlinear stochastic complex networks in ecological systems can exhibit tipping points. They can signify extinction from a survival state and, conversely, a recovery transition from extinction to survival. We investigate a control method that delays the extinction and advances the recovery by controlling the decay rate of pollinators of diverse rankings in a pollinators-plants stochastic mutualistic complex network. Our investigation is grounded on empirical networks occurring in natural habitats. We also address how the control method is affected by both environmental and demographic noises. By comparing the empirical network with the random and scale-free networks, we also study the influence of the topological structure on the control effect. Finally, we carry out a theoretical analysis using a reduced dimensional model. A remarkable result of this work is that the introduction of pollinator species in the habitat, which is immune to environmental deterioration and that is in mutualistic relationship with the collapsed ones, definitely helps in promoting the recovery. This has implications for managing ecological systems.

Astroglial Calcium Signaling Encodes Sleep Need in Drosophila

Sleep is under homeostatic control, whereby increasing wakefulness generates sleep need and triggers sleep drive. However, the molecular and cellular pathways by which sleep need is encoded are poorly understood. In addition, the mechanisms underlying both how and when sleep need is transformed to sleep drive are unknown. Here, using ex vivo and in vivo imaging, we show in Drosophila that astroglial Ca²⁺ signaling increases with sleep need. We demonstrate that this signaling is dependent on a specific L-type Ca²⁺ channel and is necessary for homeostatic sleep rebound. Thermogenetically increasing Ca²⁺ in astrocytes induces persistent sleep behavior, and we exploit this phenotype to conduct a genetic screen for genes required for the homeostatic regulation of sleep. From this large-scale screen, we identify TyrRII, a monoaminergic receptor required in astrocytes for sleep homeostasis. TyrRII levels rise following sleep deprivation in a Ca²⁺-dependent manner, promoting further increases in astrocytic Ca²⁺ and resulting in a positive-feedback loop. Moreover, our findings suggest that astrocytes then transmit this sleep need to a sleep drive circuit by upregulating and releasing the interleukin-1 analog Spätzle, which then acts on Toll receptors on R5 neurons. These findings define astroglial Ca²⁺ signaling mechanisms encoding sleep need and reveal dynamic properties of the sleep homeostatic control system.

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

Designing a Communication Field with a Transformation Method

The transformation method that was originally used to tailor the physical fields into desired spatial patterns by designing material parameters is used herein to obtain necessary local dynamic parameters when the state distribution of a network system is prescribed in space. This constitutes a typical inverse problem that controls the state distribution of a complex network by designing its local dynamic parameters. Thus, it is difficult to obtain a direct solution. This coordinate transformation provides a direct method. The feasibility of this method is demonstrated and verified by two examples (a communication field bender and a communication field cloak) in corresponding network systems.

Synthetic biology toolkits and applications in Saccharomyces cerevisiae

Synthetic biologists construct biological components and systems to look into biological phenomena and drive a myriad of practical applications that aim to tackle current global challenges in energy, healthcare and the environment. While most tools have been established in bacteria, particularly Escherichia coli, recent years have seen parallel developments in the model yeast strain Saccharomyces cerevisiae, one of the most well-understood eukaryotic biological system. Here, we outline the latest advances in yeast synthetic biology tools based on a framework of abstraction hierarchies of parts, circuits and genomes. In brief, the creation and characterization of biological parts are explored at the transcriptional, translational and post-translational levels. Using characterized parts as building block units, the designing of functional circuits is elaborated with examples. In addition, the status and potential applications of synthetic genomes as a genome level platform for biological system construction are also discussed. In addition to the development of a toolkit, we describe how those tools have been applied in the areas of drug production and screening, study of disease mechanisms, pollutant sensing and bioremediation. Finally, we provide a future outlook of yeast as a workhorse of eukaryotic genetics and a chosen chassis in this field.

Long negative feedback loop enhances period tunability of biological oscillators

Oscillatory phenomena play a major role in organisms. In some biological oscillations such as cell cycles and heartbeats, the period can be tuned without significant changes in the amplitude. This property is called (period) tunability, one of the prominent features of biological oscillations. However, how biological oscillators produce tunable oscillations remains largely unexplored. We tackle this question using computational experiments. It has been reported that positive-plus-negative feedback oscillators produce tunable oscillations through the hysteresis-based mechanism. First, in this study, we confirmed that positive-plus-negative feedback oscillators generate tunable oscillations. Second, we found that tunability is positively correlated with the dynamic range of oscillations. Third, we showed that long negative feedback oscillators without any additional positive feedback loops can produce tunable oscillations. Finally, we computationally demonstrated that by lengthening the negative feedback loop, the Repressilator, known as a non-tunable synthetic gene oscillator, can be converted into a tunable oscillator. This work provides synthetic biologists with clues to design tunable gene oscillators.

The equilibrium between antagonistic signaling pathways determines the number of synapses in Drosophila

The number of synapses is a major determinant of behavior and many neural diseases exhibit deviations in that number. However, how signaling pathways control this number is still poorly understood. Using the Drosophila larval neuromuscular junction, we show here a PI3K-dependent pathway for synaptogenesis which is functionally connected with other previously known elements including the Wit receptor, its ligand Gbb, and the MAPkinases cascade. Based on epistasis assays, we determined the functional hierarchy within the pathway. Wit seems to trigger signaling through PI3K, and Ras85D also contributes to the initiation of synaptogenesis. However, contrary to other signaling pathways, PI3K does not require Ras85D binding in the context of synaptogenesis. In addition to the MAPK cascade, Bsk/JNK undergoes regulation by Puc and Ras85D which results in a narrow range of activity of this kinase to determine normalcy of synapse number. The transcriptional readout of the synaptogenesis pathway involves the Fos/Jun complex and the repressor Cic. In addition, we identified an antagonistic pathway that uses the transcription factors Mad and Medea and the microRNA bantam to down-regulate key elements of the pro-synaptogenesis pathway. Like its counterpart, the anti-synaptogenesis signaling uses small GTPases and MAPKs including Ras64B, Ras-like-a, p38a and Licorne. Bantam downregulates the pro-synaptogenesis factors PI3K, Hiw, Ras85D and Bsk, but not AKT. AKT, however, can suppress Mad which, in conjunction with the reported suppression of Mad by Hiw, closes the mutual regulation between both pathways. Thus, the number of synapses seems to result from the balanced output from these two pathways.

An extended model for culture-dependent heterogenous gene expression and proliferation dynamics in mouse embryonic stem cells

During development, pluripotency is a transient state describing a cell's ability to give rise to all three germ layers and germline. Recent studies have shown that, in vitro, pluripotency is highly dynamic: exogenous stimuli provided to cultures of mouse embryonic stem cells, isolated from pre-implantation blastocysts, significantly affect the spectrum of pluripotency. 2i/LIF, a recently defined serum-free medium, forces mouse embryonic stem cells into a ground-state of pluripotency, while serum/LIF cultures promote the co-existence of ground-like and primed-like mouse embryonic stem cell subpopulations. The latter heterogeneity correlates with temporal fluctuations of pluripotency markers, including the master regulator Nanog, in single cells. We propose a mathematical model of Nanog dynamics in both media, accounting for recent experimental data showing the persistence of a small Nanog Low subpopulation in ground-state pluripotency mouse embryonic stem cell cultures. The model integrates into the core pluripotency Gene Regulatory Network both inhibitors present in 2i/LIF (PD and Chiron), and feedback interactions with genes found to be differentially expressed in the two media. Our simulations and bifurcation analysis show that, in ground-state cultures, Nanog dynamics result from the combination of reduced noise in gene expression and the shift of the system towards a monostable, but still excitable, regulation. Experimental data and agent-based modelling simulations indicate that mouse embryonic stem cell proliferation dynamics vary in the two media, and cannot be reproduced by accounting only for Nanog-dependent cell-cycle regulation. We further demonstrate that both PD and Chiron play a key role in regulating heterogeneity in transcription factor expression and, ultimately, mouse embryonic stem cell fate decision.

Controllability in cancer metabolic networks according to drug targets as driver nodes

Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

The PLOS ONE synthetic biology collection: six years and counting

Since it was launched in 2006, PLOS ONE has published over fifty articles illustrating the many facets of the emerging field of synthetic biology. This article reviews these publications by organizing them into broad categories focused on DNA synthesis and assembly techniques, the development of libraries of biological parts, the use of synthetic biology in protein engineering applications, and the engineering of gene regulatory networks and metabolic pathways. Finally, we review articles that describe enabling technologies such as software and modeling, along with new instrumentation. In order to increase the visibility of this body of work, the papers have been assembled into the PLOS ONE Synthetic Biology Collection (www.ploscollections.org/synbio). Many of the innovative features of the PLOS ONE web site will help make this collection a resource that will support a lively dialogue between readers and authors of PLOS ONE synthetic biology papers. The content of the collection will be updated periodically by including relevant articles as they are published by the journal. Thus, we hope that this collection will continue to meet the publishing needs of the synthetic biology community.

A strategy for building an amplified transcriptional switch to detect bacterial contamination of plants

We have designed and tested a transcriptional autofeedback loop that could be used to engineer plants to sense the presence of bacteria. The signal amplification circuit was built based on the biological switch responsive to the presence of bacterial flagellin. Several flagellin- and E. coli-inducible Arabidopsis promoters were cloned and tested in transient expression assays in Arabidopsis and lettuce protoplasts using a flagellin-based peptide. These were investigated either as direct drivers of a reporter gene, or as a component of a transcriptional autofeedback loop. Arabidopsis promoters from the xyloglucan endotransglucosylase/hydrolase 18 (ATXTH18) and cytochrome P450 family CYP82C3 monooxygenase worked well as biological switches. These promoters were incorporated into our feedback loop system for signal amplification. The inclusion of a transcriptional repressor reduced basal expression, thereby increasing fold-amplification of signal detection and fine-tuning the positive autofeedback loop regulation.

Controllability of complex networks

The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system's entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network's degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.

Linear control theory for gene network modeling

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

An environment-sensitive synthetic microbial ecosystem

Microbial ecosystems have been widely used in industrial production, but the inter-relationships of organisms within them haven't been completely clarified due to complex composition and structure of natural microbial ecosystems. So it is challenging for ecologists to get deep insights on how ecosystems function and interplay with surrounding environments. But the recent progresses in synthetic biology show that construction of artificial ecosystems where relationships of species are comparatively clear could help us further uncover the meadow of those tiny societies. By using two quorum-sensing signal transduction circuits, this research designed, simulated and constructed a synthetic ecosystem where various population dynamics formed by changing environmental factors. Coherent experimental data and mathematical simulation in our study show that different antibiotics levels and initial cell densities can result in correlated population dynamics such as extinction, obligatory mutualism, facultative mutualism and commensalism. This synthetic ecosystem provides valuable information for addressing questions in ecology and may act as a chassis for construction of more complex microbial ecosystems.