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Synthetic biology is an engineering discipline that builds on our mechanistic understanding of molecular biology to program microbes to carry out new functions. Such predictable manipulation of a cell requires modeling and experimental techniques to work together. The modeling component of synthetic biology allows one to design biological circuits and analyze its expected behavior. The experimental component merges models with real systems by providing quantitative data and sets of available biological 'parts' that can be used to construct circuits. Sufficient progress has been made in the combined use of modeling and experimental methods, which reinforces the idea of being able to use engineered microbes as a technological platform.

Cellular signaling pathways respond to external inputs to drive pivotal cellular decisions. Far from being mere data relay systems, signaling cascades form complex interacting networks with multiple layers of feedback and feed-forward control loops regulated in both space and time. While it may be intuitively obvious that this complexity allows cells to assess and respond appropriately to a myriad of external cues, untangling the wires to understand precisely how complex networks function as control and computational systems presents a daunting challenge to theoretical and experimental biologists alike. In this review we have focused on activation of the canonical MAP kinase cascade by receptor tyrosine kinases (RTKs) in order to examine some of the fundamental design principles used to build biological circuits and control systems. In particular, we explore how cells can reconfigure signaling cascades to generate distinct biological outputs by utilizing the unique spatial constraints available in biological membranes.

Molecular communications is an emergent field that seeks to develop nanoscale communication devices using design principles gleaned from studies of the topology and dynamic properties of biological signaling networks. To understand how these networks function, we must first characterize the functional building blocks that compose them, and the best candidates for those are the topologically distinct subnetworks, or motifs, that appear in a statistically improbable abundance. In transcriptional networks, one of the most prevalent motifs is the feed-forward loop, a three node motif wherein one top-level protein regulates the expression of a target gene either directly or indirectly through an intermediate regulator protein. Currently, no systematic effort has been made to treat an isolated feed-forward loop as a stand-alone signal amplifying/attenuating device and understand its communication capacity in terms of the diffusion of individual molecules. To address this issue, we derive a theorem that estimates the upper and lower bounds of the channel capacity for a relay channel, which structurally corresponds to a feed-forward loop, by using an additive inverse Gaussian noise channel model of protein-ligand binding. Our results are just a first step towards assessing the performance bounds of simplified biological circuits in order to guide the development and optimization of synthetic, bio-inspired devices that can be used as information processing and forwarding units.

Synthetic gene networks have evolved from simple proof-of-concept circuits to complex therapy-oriented networks over the past 15 years. This advancement has greatly facilitated the expansion of the emerging field of synthetic biology. In this review, we highlight the main applications of synthetic gene networks in understanding biological design principles, developing biosensors for diagnosis, producing industrial and biomedical compounds, and treating human diseases. Finally, we outline current challenges and future prospects of synthetic gene networks for advancing practical applications.

We introduce an idea of synthesizing a class of genetic registers based on the existing sequential biological circuits, which are composed of fundamental biological gates. In the renowned literature, biological gates and genetic oscillator have been unveiled and experimentally realized in recent years. These biological circuits have formed a basis for realizing a primitive biocomputer. In the traditional computer architecture, there is an intermediate load-store section, i.e. a register, which serves as a part of the digital processor. With which, the processor can load data from a larger memory into it and proceed to conduct necessary arithmetic or logic operations. Then, manipulated data are stored back to the memory by instruction via the register. We propose here a class of bio-registers for the biocomputer. Four types of register structures are presented. In silicon experiments illustrate results of the proposed design.

Background RNAs are attractive molecules as the biological parts for synthetic biology. In particular, the ability of conformational changes, which can be encoded in designer RNAs, enables us to create multistable molecular switches that function in biological circuits. Although various algorithms for designing such RNA switches have been proposed, the previous algorithms optimize the RNA sequences against the weighted sum of objective functions, where empirical weights among objective functions are used. In addition, an RNA design algorithm for multiple pseudoknot targets is currently not available. Results We developed a novel computational tool for automatically designing RNA sequences which fold into multiple target secondary structures. Our algorithm designs RNA sequences based on multi-objective genetic algorithm, by which we can explore the RNA sequences having good objective function values without empirical weight parameters among the objective functions. Our algorithm has great flexibility by virtue of this weight-free nature. We benchmarked our multi-target RNA design algorithm with the datasets of two, three, and four target structures and found that our algorithm shows better or comparable design performances compared with the previous algorithms, RNAdesign and Frnakenstein. In addition to the benchmarks with pseudoknot-free datasets, we benchmarked MODENA with two-target pseudoknot datasets and found that MODENA can design the RNAs which have the target pseudoknotted secondary structures whose free energies are close to the lowest free energy. Moreover, we applied our algorithm to a ribozyme-based ON-switch which takes a ribozyme-inactive secondary structure when the theophylline aptamer structure is assumed. Conclusions Currently, MODENA is the only RNA design software which can be applied to multiple pseudoknot targets. Successful design results for the multiple targets and an RNA device indicate usefulness of our multi-objective RNA design algorithm. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0706-x) contains supplementary material, which is available to authorized users.

We describe a $0.35~\mu{\rm m}$ BiCMOS silicon chip that quantitatively models fundamental molecular circuits via efficient log-domain cytomorphic transistor equivalents. These circuits include those for biochemical binding with automatic representation of non-modular and loading behavior, e.g., in cascade and fan-out topologies; for representing variable Hill-coefficient operation and cooperative binding; for representing inducer, transcription-factor, and DNA binding; for probabilistic gene transcription with analogic representations of log-linear and saturating operation; for gain, degradation, and dynamics of mRNA and protein variables in transcription and translation; and, for faithfully representing biological noise via tunable stochastic transistor circuits. The use of on-chip DACs and ADCs enables multiple chips to interact via incoming and outgoing molecular digital data packets and thus create scalable biochemical reaction networks. The use of off-chip digital processors and on-chip digital memory enables programmable connectivity and parameter storage. We show that published static and dynamic MATLAB models of synthetic biological circuits including repressilators, feed-forward loops, and feedback oscillators are in excellent quantitative agreement with those from transistor circuits on the chip. Computationally intensive stochastic Gillespie simulations of molecular production are also rapidly reproduced by the chip and can be reliably tuned over the range of signal-to-noise ratios observed in biological cells.

Background The interconnection of quantitatively characterized biological devices may lead to composite systems with apparently unpredictable behaviour. Context-dependent variability of biological parts has been investigated in several studies, measuring its entity and identifying the factors contributing to variability. Such studies rely on the experimental analysis of model systems, by quantifying reporter genes via population or single-cell approaches. However, cell-to-cell variability is not commonly included in predictability analyses, thus relying on predictive models trained and tested on central tendency values. This work aims to study in silico the effects of cell-to-cell variability on the population-averaged output of interconnected biological circuits. Methods The steady-state deterministic transfer function of individual devices was described by Hill equations and lognormal synthetic noise was applied to their output. Two- and three-module networks were studied, where individual devices implemented inducible/repressible functions. The single-cell output of such networks was simulated as a function of noise entity; their population-averaged output was computed and used to investigate the expected variability in transfer function identification. The study was extended by testing different noise models, module logic, intrinsic/extrinsic noise proportions and network configurations. Results First, the transfer function of an individual module was identified from simulated data of a two-module network. The estimated parameter variability among different noise entities was limited (14%), while a larger difference was observed (up to 62%) when estimated and true parameters were compared. Thus, low-variability parameter estimates can be obtained for different noise entities, although deviating from the true parameters, whose measurement requires noise knowledge. Second, the black-box input-output function of a two/three-module network was predicted from the knowledge of the transfer function of individual modules, identified in the presence of noise. Estimates variability was low (16%); however, differences up to 68% were observed by simulating a typical experimental study where the predictions obtained above were compared to network outputs generated in the presence of noise. Network predictions can, thus, deviate from real outputs when modules are characterized and re-used in different noise contexts. Conclusions The adopted approach can support predictability studies in synthetic biology by distinguishing between actual unpredictability and contribution of noise and by guiding researchers in the design of suitable experimental measurement for gene networks.

Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness, and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behavior in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organization can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system toward criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality., This paper was supported by the Spanish Ministry of Economy and Competitiveness (Grants FIS2013-44674-P and FIS2012-37655-C02-01) and FEDER and by the ICREA Academia programme (JGO). MT acknowledges support of the Army Research Office through grant W911NF-04-D-0001.

Systems biology addresses challenges in the analysis of genomics data, especially for complex genes and protein interactions using Meta data approach on various signaling pathways. In this paper, we report systems biology and biological circuits approach to construct pathway and identify early gene and protein interactions for predicting GPR142 responses in Type 2 diabetes. The information regarding genes, proteins and other molecules involved in Type 2 diabetes were retrieved from literature and kinetic simulation of GPR142 was carried out in order to determine the dynamic interactions. The major objective of this work was to design a GPR142 biochemical pathway using both systems biology as well as biological circuits synthetically. The term ‘synthetically’ refers to building biological circuits for cell signaling pathway especially for hormonal pathway disease. The focus of the paper is on logical components and logical circuits whereby using these applications users can create complex virtual circuits. Logic gates process represents only true or false and investigates whether biological regulatory circuits are active or inactive. The basic gates used are AND, NAND, OR, XOR and NOT gates and Integrated circuit composition of many such basic gates and some derived gates. Biological circuits may have a futuristic application in biomedical sciences which may involve placing a micro chip in human cells to modulate the down or up regulation of hormonal disease.

Systems biology approaches for modeling cellular signaling networks affected by chemical exposures should soon produce integrated methodologies capable of predicting dose-response relationships for developmental toxicants and for other toxic responses. This paper outlines an emerging strategy for systems biology approaches in dose-response modeling. Genome-wide functional screens, bioinformatic tools, and network mapping technologies together can provide directed graph representations of the cellular signaling networks. The graphical representations can be converted into mathematical models that permit predicting the shapes of dose-response curves for altered cell signaling by test compounds during development. Systems biology approaches require interdisciplinary teams with expertise in reproduction, cell biology, signal transduction, mathematical/biomedical modeling, and risk assessment. In addition to outlining a systems approach for dose-response research, this paper discusses initial stages of application of this strategy to examine inhibition of steroidogenesis in testes by phthalate esters.

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other ‘omics’ studies.

Success in synthetic biology depends on the efficient construction of robust genetic circuitry. However, even the direct engineering of the simplest genetic elements (switches, logic gates) is a challenge and involves intense lab work. As the complexity of biological circuits grows, it becomes more complicated and less fruitful to rely on the rational design paradigm, because it demands many time-consuming trial-and-error cycles. One of the reasons is the context-dependent behavior of small assembly parts (like BioBricks), which in a complex environment often interact in an unpredictable way. Therefore, the idea of evolutionary engineering (artificial directed in vivo evolution) based on screening and selection of randomized combinatorial genetic circuit libraries became popular. In this article we build on the so-called dual selection technique. We propose a plasmid-based framework using toxin-antitoxin pairs together with the relaxase conjugative protein, enabling an efficient autonomous in vivo evolutionary selection of simple Boolean circuits in bacteria (E. coli was chosen for demonstration). Unlike previously reported protocols, both on and off selection steps can run simultaneously in various cells in the same environment without human intervention; and good circuits not only survive the selection process but are also horizontally transferred by conjugation to the neighbor cells to accelerate the convergence rate of the selection process. Our directed evolution strategy combines a new dual selection method with fluorescence-based screening to increase the robustness of the technique against mutations. As there are more orthogonal toxin-antitoxin pairs in E. coli, the approach is likely to be scalable to more complex functions. In silico experiments based on empirical data confirm the high search and selection capability of the protocol.

Synthetic Biology is an interdisciplinary approach combining biotechnology, evolutionary biology, molecular biology, systems biology and biophysics. While the exact definition of Synthetic Biology might still be debatable, its focus on design and construction of biological devices that perform useful functions is clear and of great utility to engineering algae. This relies on the re-engineering of biological circuits and optimization of certain metabolic pathways to reprogram algae and introduce new functions in them via the use of genetic modules. Genetic editing tools are primary enabling techniques in Synthetic Biology and this chapter discusses common techniques that show promise for algal gene editing. The genetic editing tools discussed in this chapter include RNA interference (RNAi) and artificial microRNAs, RNA scaffolds, transcription activator-like effector nucleases (TALENs), RNA guided Cas9 endonucleases (CRISPR), and multiplex automated genome engineering (MAGE). DNA and whole genome synthesis is another enabling technology in Synthetic Biology and might present an alternative approach to drastically and readily modify algae. Clear and powerful examples of the potential of whole genome synthesis for algal engineering are presented. Also, the development of relevant computational tools, and genetic part registries has stimulated further advancements in the field and their utility in algal research and engineering is described. For now, the majority of synthetic biology efforts are focused on microbes as many pressing problems, such as sustainability in food and energy production rely on modification of microorganisms. Synthetic modifications of algal strains to enhance desired physiological properties could lead to improvements in their utility.

Associative learning (AL) is one of the key mechanisms displayed by living organisms in order to adapt to their changing environments. It was recognized early as a general trait of complex multicellular organisms but is also found in ‘simpler’ ones. It has also been explored within synthetic biology using molecular circuits that are directly inspired in neural network models of conditioning. These designs involve complex wiring diagrams to be implemented within one single cell, and the presence of diverse molecular wires become a challenge that might be very difficult to overcome. Here we present three alternative circuit designs based on two-cell microbial consortia able to properly display AL responses to two classes of stimuli and displaying long- and short-term memory (i.e. the association can be lost with time). These designs might be a helpful approach for engineering the human gut microbiome or even synthetic organoids, defining a new class of decision-making biological circuits capable of memory and adaptation to changing conditions. The potential implications and extensions are outlined.

We show that difficulties in regulating cellular behavior with synthetic biological circuits may be circumvented using in silico feedback control. By tracking a circuit's output in Saccharomyces cerevisiae in real time, we precisely control its behavior using an in silico feedback algorithm to compute regulatory inputs implemented through a genetically encoded light-responsive module. Moving control functions outside the cell should enable more sophisticated manipulation of cellular processes whenever real-time measurements of cellular variables are possible.

Modelling and computational simulation are crucial for the large-scale engineering of biological circuits since they allow the system under design to be simulated prior to implementation in vivo . To support automated, model-driven design it is desirable that in silico models are modular, composable and use standard formats. The synthetic biology design process typically involves the composition of genetic circuits from individual parts. At the most basic level, these parts are representations of genetic features such as promoters, ribosome binding sites (RBSs), and coding sequences (CDSs). However, it is also desirable to model the biological molecules and behaviour that arise when these parts are combined in vivo . Modular models of parts can be composed and their associated systems simulated, facilitating the process of model-centred design. The availability of databases of modular models is essential to support software tools used in the model-driven design process. In this article, we present an approach to support the development of composable, modular models for synthetic biology, termed Standard Virtual Parts. We then describe a programmatically accessible and publicly available database of these models to allow their use by computational design tools.

This chapter describes approaches for using computational modeling of synthetic biology perturbations to analyze endogenous biological circuits, with a particular focus on signaling and metabolic pathways. We describe a bottom-up approach in which ordinary differential equations are constructed to model the core interactions of a pathway of interest. We then discuss methods for modeling synthetic perturbations that can be used to investigate properties of the natural circuit. Keeping in mind the importance of the interplay between modeling and experimentation, we next describe experimental methods for constructing synthetic perturbations to test the computational predictions. Finally, we present a case study of the p53 tumor-suppressor pathway, illustrating the process of modeling the core network, designing informative synthetic perturbations in silico, and testing the predictions in vivo.

Homeostasis is a running theme in biology. Often achieved through feedback regulation strategies, homeostasis allows living cells to control their internal environment as a means for surviving changing and unfavourable environments. While many endogenous homeostatic motifs have been studied in living cells, some other motifs may remain under-explored or even undiscovered. At the same time, known regulatory motifs have been mostly analyzed at the deterministic level, and the effect of noise on their regulatory function has received low attention. Here we lay the foundation for a regulation theory at the molecular level that explicitly takes into account the noisy nature of biochemical reactions and provides novel tools for the analysis and design of robust homeostatic circuits. Using these ideas, we propose a new regulation motif, which we refer to as {\em antithetic integral feedback, and demonstrate its effectiveness as a strategy for generically regulating a wide class of reaction networks. By combining tools from probability and control theory, we show that the proposed motif preserves the stability of the overall network, steers the population of any regulated species to a desired set point, and achieves robust perfect adaptation -- all with low prior knowledge of reaction rates. Moreover, our proposed regulatory motif can be implemented using a very small number of molecules and hence has a negligible metabolic load. Strikingly, the regulatory motif exploits stochastic noise, leading to enhanced regulation in scenarios where noise-free implementations result in dysregulation. Finally, we discuss the possible manifestation of the proposed antithetic integral feedback motif in endogenous biological circuits and its realization in synthetic circuits., 20 pages, 5 figures

The ability to link systems together such that they behave as predicted once they interact with each other is an essential requirement for the forward-engineering of robust synthetic biological circuits. Unfortunately, because of context-dependencies, parts and functional modules often behave unpredictably once interacting in the cellular environment. This paper reviews recent advances toward establishing a rigorous engineering framework for insulating parts and modules from their context to improve modularity. Overall, a synergy between engineering better parts and higher-level circuit design will be important to resolve the problem of context-dependence.

Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network regulatory mechanisms remains limited. This shortcoming is of particular relevance to synthetic biology. Synthetic biological circuits tend to lose their engineered functionality over generational time, primarily due to the deleterious stress that they exert on their host organisms. To reduce this stress (and thus minimize loss of functionality) synthetic circuits must be sensitive to the health of the host organism. Development of integrated regulatory systems is therefore essential to robust synthetic biological systems. The aim of this study was to develop integrated gene-regulatory and metabolic networks which self-optimize in response to varying environmental conditions. We performed \emph{in silico} evolution to develop such networks using a two-step approach: (1) We optimized metabolic networks given a constrained amount of available enzyme. Here, we found that a proportional relationship between flux control coefficients and enzyme mass holds in all linear sub-networks of branched networks, except those sub-networks which contain allosteric regulators. Network optimization was performed by iteratively redistributing enzyme until flux through the network was maximized. Optimization was performed for a range of boundary metabolite conditions to develop a profile of optimal enzyme distributions as a function of environmental conditions. (2) We generated and evolved randomized gene regulatory networks to modulate the enzymes of a target metabolic pathway. The objective of the gene regulatory networks was to produce the optimal distribution of metabolic network enzymes given specific boundary metabolite conditions of the target network. Competitive evolutionary algorithms were applied to optimize the specific structures and kinetic parameters of the gene regulatory networks. With this method, we demonstrate the possibility of algorithmic development of integrated adaptive gene and metabolic regulatory networks which dynamically self-optimize in response to changing environmental conditions.

Saccharomyces cerevisiae can serve as a key production platform for biofuels, nutraceuticals, industrial compounds, and therapeutic proteins. Over the recent years, synthetic biology tools and libraries have expanded in yeast to provide newfound control over regulation and synthetic circuits. This review provides an update on the status of the synthetic biology toolbox in yeast for use as a cell factory. Specifically, we discuss the impact of plasmid selection and composition, promoter, terminator, transcription factor, and aptamer selection. In doing so, we highlight documented interactions between these components, current states of development, and applications that demonstrate the utility of these parts with a particular focus on synthetic gene expression control.

Two central biophysical laws describe sensory responses to input signals. One is a logarithmic relationship between input and output, and the other is a power law relationship. These laws are sometimes called the Weber-Fechner law and the Stevens power law, respectively. The two laws are found in a wide variety of human sensory systems including hearing, vision, taste, and weight perception; they also occur in the responses of cells to stimuli. However the mechanistic origin of these laws is not fully understood. To address this, we consider a class of biological circuits exhibiting a property called fold-change detection (FCD). In these circuits the response dynamics depend only on the relative change in input signal and not its absolute level, a property which applies to many physiological and cellular sensory systems. We show analytically that by changing a single parameter in the FCD circuits, both logarithmic and power-law relationships emerge; these laws are modified versions of the Weber-Fechner and Stevens laws. The parameter that determines which law is found is the steepness (effective Hill coefficient) of the effect of the internal variable on the output. This finding applies to major circuit architectures found in biological systems, including the incoherent feed-forward loop and nonlinear integral feedback loops. Therefore, if one measures the response to different fold changes in input signal and observes a logarithmic or power law, the present theory can be used to rule out certain FCD mechanisms, and to predict their cooperativity parameter. We demonstrate this approach using data from eukaryotic chemotaxis signaling., Author Summary One of the first measurements an experimentalist makes to understand a sensory system is to explore the relation between input signal and the systems response amplitude. Here, we show using mathematical models that this measurement can give important clues about the possible mechanism of sensing. We use models that incorporate the nearly-universal features of sensory systems, including hearing and vision, and the sensing pathways of individual cells. These nearly-universal features include exact adaptation-the ability to ignore prolonged input stimuli and return to basal activity, and fold-change detection- response to relative changes in input, not absolute changes. Together with information on the input-output relationship-e.g. is it a logarithmic or a power law relationship-we show that these conditions provide enough constraints to allow the researcher to reject certain circuit designs; it also predicts, if one assumes a given design, one of its key parameters. This study can thus help unify our understanding of sensory systems, and help pinpoint the possible biological circuits based on physiological measurements.

Engineered biological circuits are often disturbed by a variety of environmental factors. In batch culture, where the majority of synthetic circuit characterization occurs, environmental conditions vary as the culture matures. Turbidostats are powerful characterization tools that provide static culture environments; however, they are often expensive, especially when purchased in custom configurations, and are difficult to design and construct in a lab. Here, we present a low cost, open source multiplexed turbidostat that can be manufactured and used with minimal experience in electrical or software engineering. We demonstrate the utility of this system to profile synthetic circuit behavior in S. cerevisiae. We also demonstrate the flexibility of the design by showing that a fluorometer can be easily integrated.

Over the past few decades there has been a lot of work in the field of synthetic biology, in order to understand the underlying concepts and cast them as a problem in chemistry, mathematics or engineering. Moulding it into control engineering, the majority of the problems arise, either due to synthesizability of the designed controller (as a Biological unit) or tackling robustness in a multi-network, to achieve the desired specifications. In this paper, we stress on how a biologically synthesizable robust controller could be designed using a novel approach of Linear Matrix Inequality (LMI) based loop shaping.

Molecular Communication (MC), i.e., the exchange of information through the emission, propagation, and reception of molecules, is a promising paradigm for the interconnection of autonomous nanoscale devices, known as nanomachines. Synthetic biology techniques, and in particular the engineering of biological circuits, are enabling research towards the programming of functions within biological cells, thus paving the way for the realization of biological nanomachines. The design of MC systems built upon biological circuits is particularly interesting since cells naturally employ the MC paradigm in their interactions, and possess many of the elements required to realize this type of communication. This paper focuses on the identification and systems-theoretic modeling of a minimal subset of biological circuit elements necessary to be included in an MC system design where the message-bearing molecules are propagated via free diffusion between two cells. The system-theoretic models are here detailed in terms of transfer functions, from which analytical expressions are derived for the attenuation and the delay experienced by an information signal through the MC system. Numerical results are presented to evaluate the attenuation and delay expressions as functions of realistic biological parameters.

To coordinate their behavior and virulence and to synchronize attacks against their hosts, bacteria communicate by producing and detecting signaling molecules (autoinducers). This communication is controlled by biological circuits called quorum sensing circuits. Recently quorum sensing circuits have been recognized as an alternative target for controlling bacterial virulence and infections without the use of antibiotics. Here we model the quorum sensing process as a state transition graph. Based on this model we develop a simulation tool for the quorum sensing process in open and confined spaces. We perform a number of numerical experiments with various strategies of quorum sensing circuit regulation and we study the effectiveness of quorum sensing inhibitors. We also use network graph theory to model the complete quorum sensing system of Pseudomonas aeruginosa and construct its state space, which turned out to be very large, hierarchical, modular and scale-free.

It has been proposed' that gene-regulatory circuits with virtually any desired property can be constructed from networks of simple regulatory elements. These properties, which include multistability and oscillations, have been found in specialized gene circuits such as the bacteriophage lambda switch and the Cyanobacteria circadian oscillator. However, these behaviours have not been demonstrated in networks of non-specialized regulatory components. Here we present the construction of a genetic toggle switch-a synthetic, bistable gene-regulatory network-in Escherichia coli and provide a simple theory that predicts the conditions necessary for bistability. The toggle is constructed from any two repressible promoters arranged in a mutually inhibitory network. It is flipped between stable states using transient chemical or thermal induction and exhibits a nearly ideal switching threshold. As a practical device, the toggle switch forms a synthetic, addressable cellular memory unit and has implications for biotechnology, biocomputing and gene therapy.

We present a case study of a tangible user interface that implements novel interaction techniques for the construction of complex queries in large data sets. Our interface, Eugenie, utilizes gestural interaction with active physical tokens and a multi-touch interactive surface to aid in the collaborative design process of synthetic biological circuits. We developed new interaction techniques for navigating large hierarchical data sets and for exploring a combinatorial design space. The goal of this research is to study the effect of gestural and tangible interaction with active tokens on sense-making throughout the bio-design process.

Reliable information processing in cells requires high sensitivity to changes in the input signal but low sensitivity to random fluctuations in the transmitted signal. There are often many alternative biological circuits qualifying for this biological function. Distinguishing theses biological models and finding the most suitable one are essential, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Here, we employ the approximate Bayesian computation (ABC) method based on sequential Monte Carlo (SMC) to search for biological circuits that can maintain signaling sensitivity while minimizing noise propagation, focusing on cases where the noise is characterized by rapid fluctuations. By systematically analyzing three-component circuits, we rank these biological circuits and identify three-basic-biological-motif buffering noise while maintaining sensitivity to long-term changes in input signals. We discuss in detail a particular implementation in control of nutrient homeostasis in yeast. The principal component analysis of the posterior provides insight into the nature of the reaction between nodes.

Accelerating the pace of synthetic biology experiments requires new approaches for rapid prototyping of circuits from individual DNA regulatory elements. However, current testing standards require days to weeks due to cloning and in vivo transformation. In this work, we first characterized methods to protect linear DNA strands from exonuclease degradation in an Escherichia coli based transcription-translation cell-free system (TX-TL), as well as mechanisms of degradation. This enabled the use of linear DNA PCR products in TX-TL. We then compared expression levels and binding dynamics of different promoters on linear DNA and plasmid DNA. We also demonstrated assembly technology to rapidly build circuits entirely in vitro from separate parts. Using this strategy, we prototyped a four component genetic switch in under 8 h entirely in vitro. Rapid in vitro assembly has future applications for prototyping multiple component circuits if combined with predictive computational models.

Inspired by biological neural systems, neuromorphic devices may open up new computing paradigms to explore cognition, learning and limits of parallel computation. Here we report the demonstration of a synaptic transistor with SmNiO₃, a correlated electron system with insulator-metal transition temperature at 130°C in bulk form. Non-volatile resistance and synaptic multilevel analogue states are demonstrated by control over composition in ionic liquid-gated devices on silicon platforms. The extent of the resistance modulation can be dramatically controlled by the film microstructure. By simulating the time difference between postneuron and preneuron spikes as the input parameter of a gate bias voltage pulse, synaptic spike-timing-dependent plasticity learning behaviour is realized. The extreme sensitivity of electrical properties to defects in correlated oxides may make them a particularly suitable class of materials to realize artificial biological circuits that can be operated at and above room temperature and seamlessly integrated into conventional electronic circuits.

In many complex regulatory networks with interlinked feedback loops, the simple core circuits are sufficient to achieve the specific biological functions of the whole networks, naturally raising a question: what is the role of the additional feedback loops. By investigating the effect of an additional toggle switch on the auto-activation circuit responsible for competent switch in Bacillus subtilits and on the activator-repressor circuit responsible for cell cycle in Xenopus embryonic , the authors show that the additional toggle switch can elaborate the dynamical behaviour of both circuits. Specifically, the additional toggle switch in B. subtilits does not significantly affect the saturation level of the competent state but can tune the activation threshold (i.e. the minimal stimulus required to switch the system from the non-competent state to the competent state). For the activator-repressor circuit in X. embryonic cell cycle, the additional toggle switch can tune the oscillation frequency but does not change the oscillation amplitude. The proposed detailed results not only provide guidelines to the engineering of synthetic genetic circuits, but also imply a significant fact that additional toggle switches in a complex network are not really redundant but play a role of tuning network functions.

Ideal cell-free expression systems can theoretically emulate an in vivo cellular environment in a controlled in vitro platform. This is useful for expressing proteins and genetic circuits in a controlled manner as well as for providing a prototyping environment for synthetic biology. To achieve the latter goal, cell-free expression systems that preserve endogenous Escherichia coli transcription-translation mechanisms are able to more accurately reflect in vivo cellular dynamics than those based on T7 RNA polymerase transcription. We describe the preparation and execution of an efficient endogenous E. coli based transcription-translation (TX-TL) cell-free expression system that can produce equivalent amounts of protein as T7-based systems at a 98% cost reduction to similar commercial systems. The preparation of buffers and crude cell extract are described, as well as the execution of a three tube TX-TL reaction. The entire protocol takes five days to prepare and yields enough material for up to 3000 single reactions in one preparation. Once prepared, each reaction takes under 8 hr from setup to data collection and analysis. Mechanisms of regulation and transcription exogenous to E. coli, such as lac/tet repressors and T7 RNA polymerase, can be supplemented.6 Endogenous properties, such as mRNA and DNA degradation rates, can also be adjusted. The TX-TL cell-free expression system has been demonstrated for large-scale circuit assembly, exploring biological phenomena, and expression of proteins under both T7- and endogenous promoters. Accompanying mathematical models are available. The resulting system has unique applications in synthetic biology as a prototyping environment, or "TX-TL biomolecular breadboard."

The question what behaviors can be exhibited by a given electronic circuit upon variation of the parameters, is fundamental to electrical engineering. To efficiently adapt behavior according to need, say from stability to instability, or among stable or unstable periodicities, it is crucial to know how systems (generically and specifically) depend on parameters. Shrimps or swallow-tails are generic, characteristic parameter space regions that yield fixed stable periodic behavior. By dividing the parameter space into stable and unstable dynamical behavior, they provide such guidelines. In applications, shrimps have first been described at great details in the context of laser systems and electronic circuits, but it is still unknown whether they could also be found in realistic models of biophysics (and if so, whether biology exploits this as an alternative computing paradigm). Here, we provide first explicit examples of their existence.

The circadian clock governs biological timekeeping on a systemic level, helping to regulate and maintain physiological processes, including endocrine and metabolic pathways with a periodicity of 24-hours. Disruption within the circadian clock machinery has been linked to numerous pathological conditions, including cancer, suggesting that clock-dependent regulation of the cell cycle is an essential control mechanism. This review will highlight recent advances on the 'gating' controls of the circadian clock at various checkpoints of the cell cycle and also how the cell cycle can influence biological rhythms. The reciprocal influence that the circadian clock and cell cycle exert on each other suggests that these intertwined biological circuits are essential and multiple regulatory/control steps have been instated to ensure proper timekeeping.

We present a new approach for the design of a synthetic biological circuit whose behaviour is specified in terms of signal temporal logic (STL) formulae. We first show how to characterise with STL formulae the input/output behaviour of biological modules miming the classical logical gates (AND, NOT, OR). Hence, we provide the regions of the parameter space for which these specifications are satisfied. Given a STL specification of the target circuit to be designed and the networks of its constituent components, we propose a methodology to constrain the behaviour of each module, then identifying the subset of the parameter space in which those constraints are satisfied, providing also a measure of the robustness for the target circuit design. This approach, which leverages recent results on the quantitative semantics of Signal Temporal Logic, is illustrated by synthesising a biological implementation of an half-adder.

Biological switches must sense changes in signal concentration and at the same time buffer against signal noise. While many studies have focused on the response of switching systems to noise in the ON state, how systems buffer noise at both ON and OFF states is poorly understood. Through analytical and computational approaches, we find that switching systems require different dynamics at the OFF state than at the ON state in order to have good noise buffering capability. Specifically, we introduce a quantity called the input-associated Signed Activation Time (iSAT) that concisely captures an intrinsic temporal property at either the ON or OFF state. We discover a trade-off between achieving good noise buffering in the ON versus the OFF states: a large iSAT corresponds to noise amplification in the OFF state in contrast to noise buffering in the ON state. To search for biological circuits that can buffer noise in both ON and OFF states, we systematically analyze all three-node circuits and identify mutual activation as a central motif. We also study connections among signal sensitivity, iSAT, and noise amplification. We find that a large iSAT at the ON state maintains signaling sensitivity while minimizing noise propagation. Taken together, the analysis of iSATs helps reveal the noise properties of biological networks and should aid in the design of robust switches that can both repress noise at the OFF state and maintain a reliable ON state. © 2013 American Chemical Society.

Synthetic biology enables bioengineers to generate biological systems that carry out a specified plan. Understanding factors that enable predictable gene regulation and expression can be useful for construction of predictable biological circuits in this field. In these experiments, we explore the effect of multiple consecutive lac promoters on gene expression. We measure the amount of Beta-galactosidase gene expression from plasmids that contain increasing numbers of lac promoters and combine experimental results with mathematical modeling. We find an increase from one to three promoters results in greater range of gene expression and a mathematical model generated based on experimental data predicts a further increase in number of regulation units results in further increase in gene expression range.

A recurring theme in biological circuits is the existence of components that are antagonistically bifunctional, in the sense that they simultaneously have two opposing effects on the same target or biological process. Examples include bifunctional enzymes that carry out two opposing reactions such as phosphorylating and dephosphorylating the same target, regulators that activate and also repress a gene in circuits called incoherent feedforward loops, and cytokines that signal immune cells to both proliferate and die. Such components are termed "paradoxical", and in this review we discuss how they can provide useful features to cell circuits that are otherwise difficult to achieve. In particular, we summarize how paradoxical components can provide robustness, generate temporal pulses, and provide fold-change detection, in which circuits respond to relative rather than absolute changes in signals.

New applications in chemical and pharmaceutical production, and advanced applications in cell-based therapies and diagnostics, as well as agricultural and environmental engineering are demanding biological circuits of increasing complexity. Synthetic biological approaches to these applications, which require well-characterized biological components and hosts to increase the efficiency and effectiveness of design and manufacture, have gained traction over the last decade as the technologies to support such efforts have improved. Knowledge of the nature and function of natural and designed biological components is quickly expanding; the ability to synthesize, assemble, and implant systems designed from these parts is following a super-exponential trend of increasing cost-effectiveness; and the ability to characterize the resultant circuits in relatively high throughput is rapidly growing. However, the heterogeneous biophysical chemistry of parts coupled with the inherent complexities of biological systems often lead to unpredictable behaviors that result from parasitic or otherwise undesired interactions among parts and with the host. This has limited the size and complexity of the applications that can be addressed directly with rational design from parts. Here, we discuss strategies to generate robust libraries of ‘standard’ parts that can be combined with directed evolution and design of experimental methods to yield effective biological circuit design cycles.

International audience; Embryogenesis is a dynamic process with an intrinsic variability whose understanding requires the integration of molecular, genetic, and cellular dynamics. Biological circuits function over time at the level of single cells and require a precise analysis of the topology, temporality, and probability of events. Integrative developmental biology is currently looking for the appropriate strategies to capture the intrinsic properties of biological systems. The "-omic" approaches require disruption of the function of the biological circuit; they provide static information, with low temporal resolution and usually with population averaging that masks fast or variable features at the cellular scale and in a single individual. This data should be correlated with cell behavior as cells are the integrators of biological activity. Cellular dynamics are captured by the in vivo microscopy observation of live organisms. This can be used to reconstruct the 3D + time cell lineage tree to serve as the basis for modeling the organism's multiscale dynamics. We discuss here the progress that has been made in this direction, starting with the reconstruction over time of three-dimensional digital embryos from in toto time-lapse imaging. Digital specimens provide the means for a quantitative description of the development of model organisms that can be stored, shared, and compared. They open the way to in silico experimentation and to a more theoretical approach to biological processes. We show, with some unpublished results, how the proposed methodology can be applied to sea urchin species that have been model organisms in the field of classical embryology and modern developmental biology for over a century.

Summary The integration of synthetic and cell-free biology has made tremendous strides towards creating artificial cellular nanosystems using concepts from solution-based chemistry: only the concentrations of reacting species modulate gene expression rates. However, it is known that macromolecular crowding, a key feature of natural cells, can dramatically influence biochemical kinetics by volume exclusion effects that reduce diffusion rates and enhance binding rates of macromolecules. Here, we demonstrate that macromolecular crowding can increase the robustness of gene expression through integrating synthetic cellular components of biological circuits and artificial cellular nanosystems. In addition, we reveal how ubiquitous cellular modules, including genetic components, a negative feedback loop, and the size of crowding molecules, can fine tune gene circuit response to molecular crowding. By bridging a key gap between artificial and living cells, our work has implications for efficient and robust control of both synthetic and natural cellular circuits.

Synthetic biology has made tremendous recent strides in constructing artificial cellular systems using minimal cell components ex vivo, creating an experimental platform for characterizing the behavior of isolated cellular modules and a form of biotechnology for the controlled operation of artificial cells. The robustness and efficiency of these systems are nonetheless challenging to control, in part because artificial cells establish an environment that is still very different from that of actual living cells. Here, we present a novel approach towards bridging the gap between artificial and true cell environments by developing artificial cells incorporating controlled macromolecular crowding, mimicking a key feature of natural cells known to dramatically influence biochemical kinetics. We demonstrate the value of our approach by showing that molecular crowding enhances gene expression and confers robustness against perturbations of gene environments. We further elucidate the underlying mechanisms of these phenomena at the single molecule level by demonstrating how large crowding molecules decrease diffusion of T7 RNA polymerase, but increase its binding to a T7 RNAP promoter. Based on single-molecule results, we further show that the impact of molecular crowding on gene circuits is enhanced by weak genetic components and maximized by a negative feedback loop. By bridging a key gap between artificial cell technology and the environment of living cells, we demonstrate the importance of intracellular crowding to efficient and robust function of biological circuits and suggest new engineering principles for controlled modulation of synthetic genetic systems.

Many biological regulatory systems process signals out of steady state and respond with a physiological delay. A simple model of regulation which respects these features shows how the ability of a delayed output to transmit information is limited: at short times by the timescale of the dynamic input, at long times by that of the dynamic output. We find that topologies of maximally informative networks correspond to commonly occurring biological circuits linked to stress response and that circuits functioning out of steady state may exploit absorbing states to transmit information optimally., Comment: 14 pages, 8 figures

A major task of study on ligand discrimination by T cells is the construction of a mechanistic model to account for threshold setting in response to variant ligands interacting with the same T-cell receptors. Recently, Lalanne and Francois in a seminal paper (2013 Phys. Rev. Lett. 110 218102) have addressed this question by constructing minimal core circuits such that the biological outputs can satisfy the essential properties of early T-cell activation. To make this core set of network topology a valuable tool for synthetic biologists to robustly engineer biological circuits, we are motivated to ask a general question: is adaptive response encoded by the proposed circuit topology structurally stable, regardless of the values of the kinetic parameters? This has particularly relevant effects for the network reliability, since failures in ligand discrimination result in either infection or autoimmune diseases. To the best of our knowledge, a rigorous and complete mathematical proof of this issue is still lacking in the literature. In this paper, by giving a rigorous mathematical proof, we have shown that this regulatory circuitry is appropriately designed and the existence, uniqueness, and globally asymptotic attractiveness of the steady state are preserved. Moreover, we further generalize the adaptive sorting module and undertake an extensive analysis on the trade-off between antagonism and sensitivity of T-cell ligand discrimination in various cellular conditions. Notably, the optimal phosphorylation step in which to place the regulatory motif is analytically obtained and numerically confirmed. Finally, relevant experimental facts and biological implications are discussed.

Circuit designers are increasingly more drawn to challenges in modeling and designing biological circuits and systems. While the principles of biological organization and architecture resemble those in systems that engineers are designing, the complexity of biological systems still seems to be beyond the designed ones. This session discusses state-of-the-art in tackling such challenges, and presents existing methods for automation of model development, design and analysis of biological circuits and systems. The speakers are experts from systems biology, synthetic biology, and design automation fields. The three talks will cover a range of topics that include rule-based modeling approach to model cell signaling networks, automation of genetic circuit design, and the importance and development of standards in synthetic biology.

In many complex regulatory networks with interlinked feedback loops, the simple core circuits are sufficient to achieve specific biological functions of the entire networks, naturally raising a question: what is the role of the additional feedback loops. Here, by investigating auto-activation and activator-repressor circuits, two most common functional motifs in regulatory networks, we show that the toggle switch acts as a tinker to elaborate the dynamical behavior of both circuits. Specifically, the additional loop does not significantly affect the stable states of the auto-activation circuit but can tune the stimulation threshold for switch (i.e., the minimal stimulus required to switch the system from the low to the high state). For the activator- repressor circuit, the tinker can tune the stimulation threshold for oscillation (i.e., the bifurcation point to generate oscillations) as well as the oscillation frequency but does not change the oscillation amplitude. These detailed results not only provide guidelines to the engineering of both synthetic circuits but also imply a significant fact that additional loops of the core circuit in a complex network are not really redundant but play a role of tuning the network's function