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Systems and Synthetic Biology are complementary fields emerging side-by-side into mainstream scientific research. Whilst systems biologists focus on understanding natural systems, synthetic biologists wish to modify, adapt and re-purpose biological systems towards certain desired goals, for example enhancing efficiency and robustness of desired biological traits. In both fields, data analysis, predictive mathematical modelling, experimental design, and controlled experimentation are crucial to obtain reproducible results and understand how applications can be scaled to larger systems and processes. As such, students from Life Sciences, Engineering, and Mathematics backgrounds must be taught fundamentals in biological systems, experimental techniques, mathematics, and data analysis/statistics. In addition, students must be trained for future multidisciplinary careers, where the interaction and communication between experimental and modelling researchers is fundamental. With the acceleration of technological developments (both computational and experimental) continuing unabated, educators need to bridge the increasing gap between fundamentally-required knowledge and skills that students need to pursue future academic or industrial research projects. In this paper, we will discuss how we have re-designed an introductory course in Systems and Synthetic Biology at Wageningen University and Research (Netherlands) that is targeted simultaneously to mathematical/computational students with an interest in biology and experimental methods, and to Life Science students interested in learning how biological systems can be mathematically analysed and modelled. The course highlights the links between fundamental methodologies and recently developed technologies within the Systems and Synthetic Biology fields. The course was re-designed for the 2021/22 academic year, we report that students from biology and biotechnology programmes graded their satisfaction of the course as 4.4 out of 5. We discuss how the course can act as a gateway to advanced courses in Systems Biology-oriented curricula (comprising: data infrastructure, modelling, and experimental synthetic biology), and towards future research projects.

Background Synthetic biological circuits are widely utilized to control microbial cell functions. Natural and synthetic riboswitches are attractive sensor modules for use in synthetic biology applications. However, tuning the fold-change of riboswitch circuits is challenging because a deep understanding of the riboswitch mechanism and screening of mutant libraries is generally required. Therefore, novel molecular parts and strategies for straightforward tuning of the fold-change of riboswitch circuits are needed. Results In this study, we devised a toehold switch-based modulator approach that combines a hybrid input construct consisting of a riboswitch and transcriptional repressor and de-novo-designed riboregulators named toehold switches. First, the introduction of a pair of toehold switches and triggers as a downstream signal-processing module to the hybrid input for coenzyme B12 resulted in a functional riboswitch circuit. Next, several optimization strategies that focused on balancing the expression levels of the RNA components greatly improved the fold-change from 260- to 887-fold depending on the promoter and host strain. Further characterizations confirmed low leakiness and high orthogonality of five toehold switch pairs, indicating the broad applicability of this strategy to riboswitch tuning. Conclusions The toehold switch-based modulator substantially improved the fold-change compared to the previous sensors with only the hybrid input construct. The programmable RNA-RNA interactions amenable to in silico design and optimization can facilitate further development of RNA-based genetic modulators for flexible tuning of riboswitch circuitry and synthetic biosensors.

The use of short peptide tags in synthetic genetic circuits allows for the tuning of gene expression dynamics and release of amino acid resources through targeted protein degradation. Here, we use elements of the Escherichia coli and Mesoplasma florum transfer-messenger RNA (tmRNA) ribosome rescue systems to compare endogenous and foreign proteolysis systems in E. coli. We characterize the performance and burden of each and show that while both greatly shorten the half-life of a tagged protein, the endogenous system is approximately ten times more efficient. Based on these results, we then demonstrate using mathematical modelling and experiments how proteolysis can improve cellular robustness through targeted degradation of a reporter protein in auxotrophic strains, providing a limited secondary source of essential amino acids that help partially restore growth when nutrients become scarce. These findings provide avenues for controlling the functional lifetime of engineered cells once deployed and increasing their tolerance to fluctuations in nutrient availability.

The recent development of synthetic biology has expanded the capability to design and construct protein networks outside of living cells from the bottom-up. The new capability has enabled us to assemble protein networks for the basic study of cellular pathways, expression of proteins outside cells, and building tissue materials. Furthermore, the integration of natural and synthetic protein networks has enabled new functions of synthetic or artificial cells. Here, we review the underlying technologies for assembling protein networks in liposomes, water-in-oil droplets, and biomaterials from the bottom-up. We cover the recent applications of protein networks in biological transduction pathways, energy self-supplying systems, cellular environmental sensors, and cell-free protein scaffolds. We also review new technologies for assembling protein networks, including multiprotein purification methods, high-throughput assay screen platforms, and controllable fusion of liposomes. Finally, we present existing challenges towards building protein networks that rival the complexity and dynamic response akin to natural systems. This review addresses the gap in our understanding of synthetic and natural protein networks. It presents a vision towards developing smart and resilient protein networks for various biomedical applications.

Control of gene expression is crucial for several biotechnological applications, especially for implementing predictable and controllable genetic circuits. Such circuits are often implemented with a transcriptional regulator activated by a specific signal. These regulators should work independently of the host machinery, with low gratuitous induction or crosstalk with host components. Moreover, the signal should also be orthogonal, recognized only by the regulator with minimal interference with the host operation. In this context, transcriptional regulators activated by plant metabolites as flavonoids emerge as candidates to control gene expression in bacteria. However, engineering novel circuits requires the characterization of the genetic parts (e.g., genes, promoters, ribosome binding sites, and terminators) in the host of interest. Therefore, we decomposed the QdoR regulatory system of B. subtilis, responsive to the flavonoid quercetin, and reassembled its parts into genetic circuits programmed to have different levels of gene expression and noise dependent on the concentration of quercetin. We showed that only one of the promoters regulated by QdoR worked well in E. coli, enabling the construction of other circuits induced by quercetin. The QdoR expression was modulated with constitutive promoters of different transcriptional strengths, leading to low expression levels when QdoR was highly expressed and vice versa. E. coli strains expressing high and low levels of QdoR were mixed and induced with the same quercetin concentration, resulting in two stable populations expressing different levels of their gene reporters. Besides, we demonstrated that the level of QdoR repression generated different noise levels in gene expression dependent on the concentration of quercetin. The circuits presented here can be exploited in applications requiring adjustment of gene expression and noise using a highly available and natural inducer as quercetin.

In this work, a gold biosensor has been constructed and characterised in Escherichia coli ( E. coli ). Though gold biosensors have been previously reported, a NOT gate gold biosensing genetic circuit was constructed using an orthogonal inverter system as an alternative to a circuit where output is contingent solely on input. This enables the detection of changes in gold concentration whereby circuit remains OFF in presence of inducer and ON in absence of inducer. The proposed biosensor is an alternative to conventional forms of gold detection such as Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). The constructed biosensor is a collection of components – golTSB operon (isolated from Salmonella enterica ), mf -Lon protease (isolated from Mesoplasma florum ) and associated ssrA degradation tag with GFP acting as a reporter. The minimum time and concentration required by the biosensor gol- mf lon-GFP (GMG) to achieve the ON output is characterised here using plate reader data derived from a single multi-stage assay.

Synthetic biology, through genetic circuit engineering in biological cells, is paving the way toward the realization of programmable man-made living devices, able to naturally operate within normally less accessible domains, i.e. , the biological and the nanoscale. The control of the information processing and exchange between these engineered-cell devices, based on molecules and biochemical reactions, i.e. , molecular communication (MC), will be enabling technologies for the emerging paradigm of the Internet of Bio-Nano Things, with applications ranging from tissue engineering to bioremediation. In this paper, the design of genetic circuits to enable MC links between engineered cells is proposed by stemming from techniques for information coding and inspired by recent studies favoring the efficiency of analog computation over digital in biological cells. In particular, the design of a joint encoder-modulator for the transmission of binary-modulated molecule concentration is coupled with a decoder that computes the a-posteriori log-likelihood ratio of the information bits from the propagated concentration. These functionalities are implemented entirely in the biochemical domain through activation and repression of genes, and biochemical reactions, rather than classical electrical circuits. Biochemical simulations are used to evaluate the proposed design against a theoretical encoder/decoder implementation taking into account impairments introduced by diffusion noise.

Boolean NOR gates have been widely implemented in Escherichia coli as transcriptional regulatory devices for building complex genetic circuits. Yet, their portability to other bacterial hosts/chassis is generally hampered by frequent changes in the parameters of the INPUT/OUTPUT response functions brought about by new genetic and biochemical contexts. Here, we have used the circuit design tool CELLO for assembling a NOR gate in the soil bacterium and the metabolic engineering platform Pseudomonas putida with components tailored for E. coli. To this end, we capitalized on the functional parameters of 20 genetic inverters for each host and the resulting compatibility between NOT pairs. Moreover, we added to the gate library three inducible promoters that are specific to P. putida, thus expanding cross-platform assembly options. While the number of potential connectable inverters decreased drastically when moving the library from E. coli to P. putida, the CELLO software was still able to find an effective NOR gate in the new chassis. The automated generation of the corresponding DNA sequence and in vivo experimental verification accredited that some genetic modules initially optimized for E. coli can indeed be reused to deliver NOR logic in P. putida as well. Furthermore, the results highlight the value of creating host-specific collections of well-characterized regulatory inverters for the quick assembly of genetic circuits to meet complex specifications., Graphical Abstract

Centro de Biotecnología y Genómica de Plantas (CBGP), Boolean NOR gates have been widely implemented in Escherichia coli as transcriptional regulatory devices for building complex genetic circuits. Yet, their portability to other bacterial hosts/chassis is generally hampered by frequent changes in the parameters of the INPUT/OUTPUT response functions brought about by new genetic and biochemical contexts. Here, we have used the circuit design tool CELLO for assembling a NOR gate in the soil bacterium and the metabolic engineering platform Pseudomonas putida with components tailored for E. coli. To this end, we capitalized on the functional parameters of 20 genetic inverters for each host and the resulting compatibility between NOT pairs. Moreover, we added to the gate library three inducible promoters that are specific to P. putida, thus expanding cross-platform assembly options. While the number of potential connectable inverters decreased drastically when moving the library from E. coli to P. putida, the CELLO software was still able to find an effective NOR gate in the new chassis. The automated generation of the corresponding DNA sequence and in vivo experimental verification accredited that some genetic modules initially optimized for E. coli can indeed be reused to deliver NOR logic in P. putida as well. Furthermore, the results highlight the value of creating host-specific collections of well-characterized regulatory inverters for the quick assembly of genetic circuits to meet complex specifications., SETH [RTI2018-095584-B-C42; MINECO/FEDER] and SyCoLiM [ERA-COBIOTECH 2018—PCI2019-111859-2] Projects of the Spanish Ministry of Science and Innovation; the MADONNA [H2020-FET-OPEN-RIA-2017-1-766975]; BioRoboost [H2020-NMBP-BIO-CSA-2018-820699]; SynBio4Flav [H2020-NMBP-TR-IND/H2020-NMBP-BIO-2018-814650] and MIX-UP [MIX-UP H2020-BIO-CN-2019-870294] Contracts of the European Union; the InGEMICSCM [S2017/BMD-3691 FSE, FECER] and BioSinT-CM [Y2020/TCS-6555] Projects of the Comunidad de Madrid. A.G.-M. was also supported by the CONTEXT project from Comunidad de Madrid [Atracción de Talento Program; 2019-T1/BIO-14053] and the Severo Ochoa Program for Centres of Excellence in R&D from the Agencia Estatal de Investigación of Spain [SEV-2016-0672] (2017–2021)., 6 Pág.

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.

Genetic circuits have been developed for quantitative measurement of enzyme activity, metabolic engineering of strain development, and dynamic regulation of microbial cells. A genetic circuit consists of several bio-elements, including enzymes and regulatory cassettes, that can generate the desired output signal, which is then used as a precise criterion for enzyme screening and engineering. Antagonists and inhibitors are small molecules with inhibitory effects on regulators and enzymes, respectively. In this study, an antagonist and an inhibitor were applied to a genetic circuit for a dynamic detection range. We developed a genetic circuit relying on regulators and enzymes, allowing for straightforward control of its output signal without additional genetic modification. We used para-nitrophenol and alanine as an antagonist of DmpR and inhibitor of tyrosine phenol-lyase, respectively. We show that the antagonist resets the detection range of the genetic circuit similarly to a resistor in an electrical logic circuit. These biological resistors in genetic circuits can be used as a rapid and precise controller of variable outputs with minimal circuit configuration.

Continuous contamination of the environment with xenobiotics and related recalcitrant compounds has emerged as a serious pollution threat. Bioremediation is the key to eliminating persistent contaminants from the environment. Traditional bioremediation processes show limitations, therefore it is necessary to discover new bioremediation technologies for better results. In this review we provide an outlook of alternative strategies for bioremediation via synthetic biology, including exploring the prerequisites for analysis of research data for developing synthetic biological models of microbial bioremediation. Moreover, cell coordination in synthetic microbial community, cell signaling, and quorum sensing as engineered for enhanced bioremediation strategies are described, along with promising gene editing tools for obtaining the host with target gene sequences responsible for the degradation of recalcitrant compounds. The synthetic genetic circuit and two-component regulatory system (TCRS)-based microbial biosensors for detection and bioremediation are also briefly explained. These developments are expected to increase the efficiency of bioremediation strategies for best results.

The engineering of biology strives on the creation of biological devices concerning society-impact applications. In this PhD thesis, we developed mathematical and experimental tools for the standard and rational design of living devices for biomedical purposes, offering robust and reliable responses. By breaking-up cellular device complexity into functional modules, we have analysed how extracellular information is detected, processed and transformed thanks to re-engineering intrinsic cellular components. We show how the desired range of action of a biosensor could be tuned by modifying the relative levels from two-component receptors’ biosensors. Regarding information processing, combining multicellularity and space permits to develop a 2D multi-branch approach inspired from printed electronics, allowing to perform logic computation by transferring device complexity into the geometrical arrangement. Sensing and processing capabilities have been applied as a proof-of-concept for the design of cellular devices for Diabetes Mellitus. Treating the cellular device closed-loop response as the fourth-functional module allowed to in silico decipher device characteristics on glycaemia regulation and design novel strategies based on dietary modulation, putting the manifest the need to combine both experimental and computational tools for living device application-based designs. L’aplicació de principis d’enginyeria en biologia permet somniar en l’ús de dispositius biològics per abordar problemes de la societat. Concretament, en aquesta tesi doctoral, s’ha abordat el disseny de dispositius biològics per aplicacions biomèdiques mitjançant la combinació d’eines experimentals i computacionals. La creació d’aquests dispositius demana d’un disseny racional que ofereixi respostes robustes i fiables. L’estudi de la creació de dispositius biològics s’ha fet seguint una aproximació modular, on s’ha analitzat com es poden re-enginyeritzar components cel·lulars per obtenir una resposta que s’adeqüi a l’aplicació requerida. Hem demostrat com podem modular el rang de detecció de la capa sensora a través de la modulació de l’element receptor de sensors bastats en dos components. Hem analitzat com integrar informació de diferents fonts de manera sistemàtica i robusta introduint com a nou element de computació l’espai i la divisió de tasques; tot desenvolupant un marc teòric i validant experimentalment per un seguit de funcions lògiques. Finalment, hem desenvolupat dispositius biològics que responen a molècules fisiològiques. Concretament, hem abordat el disseny de dispositius biològics pel tractament de la Diabetes Mellitus. Una primera validació experimental ens ha permès establir l’ús d’aquests dispositius in vitro. Seguidament, hem aprofundit en l’estudi de la seva aplicació mitjançant l’ús d’un simulador de pacient diabètic que ens ha permès el seu tractament virtual i l’anàlisi de les característiques del dispositiu per la regulació de la glicèmia. Finalment, hem explorat com la combinació dels dispositius cel·lulars amb la regulació del patró d’ingestes introdueix millores en els nivells de glucosa en sang. Posant de manifest el potencial que ofereix la creació d’una plataforma hibrida pel disseny de dispositius cel·lulars per una determinada aplicació.

Cell therapies utilize functions of the entire living cell to fight diseases. The first chimeric antigen receptor T-cells are already approved for cancer treatment and many others are being developed. Furthermore, multiple proof-of-concept animal studies showed promising results in the treatment of metabolic diseases using designer cells. To face the challenges of their clinical translation, new purpose-driven methods to regulate the behavior of engineered cells are required. Electronic devices can collect numerous diagnostic or biometric data. Combining them with therapeutic cells to create hybrid, bioelectronic systems could leverage strengths of both components. To achieve this, it would be essential to create an electrogenetic interface that translates the information collected by electronic devices to a format which engineered cells are able to interpret. CHAPTER I of this thesis presents the first direct interface between therapeutic cells and electronic devices. Electrical stimulation opens L-type voltage gated calcium channels and causes calcium influx, which can be linked to either transgene expression, or fast vesicular secretion. The resulting engineered human cell line Electroβ is capable of rapid insulin secretion in the timescale of minutes, bypassing the transcriptional delay typical for current synthetic systems. A wireless-powered subcutaneous implant encapsulating Electroβ cells can provide real-time control of glycaemia in a type I diabetes mouse model. Such electrogenetic devices offer new opportunities for advanced healthcare in the future. Reprogramming of cellular behavior typically focuses on achieving an input-specific reaction. Current designs that rely on engineered receptors are limited to single inputs, and often suffer from high leakiness and low fold induction. CHAPTER II of this thesis focuses on a new method of rewiring of endogenous signaling pathways to alternative genomic targets, to upregulate expression of genes important for therapeutic purposes. It introduces Generalized Engineered Activation Regulators (GEARs) that consist of the MS2 bacteriophage coat protein fused to regulatory or transactivation domains. GEARs are driven by catalytically inactive Cas9 (dCas9), and can hijack intracellular signaling dependent on NFAT, NFκB, SMAD2 and Elk1. Because of being pathway-specific, they can integrate and process multiple input signals. GEARs enable a membrane depolarization-induced activation of insulin production in β mimetic cells, interleukin 12 expression in activated immortalized T-cells (Jurkat), interleukin 12 production in response to the immunomodulatory cytokines TGFβ and TNFα in HEK293T cells, as well as a simultaneous activation of two genes. Engineered cells with the ability to reinterpret their behavioral programs have potential for applications in immunotherapy and in the treatment of metabolic diseases.

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.

CRISPR-based enzymes have offered a unique capability to the design of genetic switches, with advantages in designability, modularity and orthogonality. CRISPR-based genetic switches operate on multiple levels of life, including transcription and translation. In both prokaryotic and eukaryotic cells, deactivated CRISPR endonuclease and endoribonuclease have served in genetic switches for activating or repressing gene expression, at both transcriptional and translational levels. With these genetic switches, more complex circuits have been assembled to achieve sophisticated functions including inducible switches, non-linear response and logical biocomputation. As more CRISPR enzymes continue to be excavated, CRISPR-based genetic switches will be used in a much wider range of applications.

Synthetic biology has seen an explosive growth in the capability of engineering artificial gene circuits from transcription factors (TFs), particularly in bacteria. However, most artificial networks still employ the same core set of TFs (for example LacI, TetR and cI). The TFs mostly function via repression and it is difficult to integrate multiple inputs in promoter logic. Here we present to our knowledge the first set of dual activator-repressor switches for orthogonal logic gates, based on bacteriophage λ cI variants and multi-input promoter architectures. Our toolkit contains 12 TFs, flexibly operating as activators, repressors, dual activator–repressors or dual repressor–repressors, on up to 270 synthetic promoters. To engineer non cross-reacting cI variants, we design a new M13 phagemid-based system for the directed evolution of biomolecules. Because cI is used in so many synthetic biology projects, the new set of variants will easily slot into the existing projects of other groups, greatly expanding current engineering capacities., Genetic circuits usually employ the same set of transcription factors which can act via repression or activation of the target promoter. Here the authors present dual activator-repressor switches, designed via directed evolution, for orthogonal logic gates and multi-input circuit architectures.

A primary objective of synthetic biology is the construction of genetic circuits with behaviors that can be predicted based on the properties of the constituent genetic parts from which they are built. However a significant issue in the construction of synthetic genetic circuits is a phenomenon known as context dependence in which the behavior of a given part changes depending on the choice of adjacent or nearby parts. Interactions between parts compromise the modularity of the circuit, impeding the implementation of predictable genetic constructs. To address this issue, investigators have devised genetic insulators that prevent these unintended context-dependent interactions between neighboring parts. One of the most commonly used insulators in bacterial systems is the self-cleaving ribozyme RiboJ. Despite its utility as an insulator, there has been no systematic quantitative assessment of the effect of RiboJ on the expression level of downstream genetic parts. Here, we characterized the impact of insulation with RiboJ on expression of a reporter gene driven by a promoter from a library of 24 frequently employed constitutive promoters in an Escherichia coli model system. We show that, depending on the strength of the promoter, insulation with RiboJ increased protein abundance between twofold and tenfold and increased transcript abundance by an average of twofold. This result demonstrates that genetic insulators in E. coli can impact the expression of downstream genes, information that is essential for the design of predictable genetic circuits and constructs. Electronic supplementary material The online version of this article (10.1186/s13036-018-0115-6) contains supplementary material, which is available to authorized users.

The dimerization and high-order oligomerization of transcription factors has endowed them with cooperative regulatory capabilities that play important roles in many cellular functions. However, such advanced regulatory capabilities have not been fully exploited in synthetic biology and genetic engineering. Here, we engineered a C-terminally fused oligomerization domain to improve the cooperativity of transcription factors. First, we found that two of three designed oligomerization domains significantly increased the cooperativity and ultrasensitivity of a transcription factor for the regulated promoter. Then, seven additional transcription factors were used to assess the modularity of the oligomerization domains, and their ultrasensitivity was generally improved, as assessed by their Hill coefficients. Moreover, we also demonstrated that the allosteric capability of the ligand-responsive domain remained intact when fusing with the designed oligomerization domain. As an example application, we showed that the engineered ultrasensitive transcription factor could be used to significantly improve the performance of a "stripe-forming" gene circuit. We envision that the oligomerization modules engineered in this study could act as a powerful tool to rapidly tune the underlying response profiles of synthetic gene circuits and metabolic pathway controllers.

The design and synthesis of novel genes and deoxyribonucleic acid (DNA) sequences is a central technique in synthetic biology. Current methods of high throughput gene synthesis use pooled oligonucleotides obtained from custom-designed DNA microarray chips, and rely on orthogonal (non-interacting) polymerase chain reaction primers to specifically de-multiplex, by amplification, the precise subset of oligonucleotides necessary to assemble a full length gene. The availability of a large validated set of mutually orthogonal primers is therefore a crucial reagent for high-throughput gene synthesis. Here, we present a set of 166 20-nucleotide primers that are experimentally verified to be non-interacting, capable of specifying 13 695 unique genes. These primers represent a valuable resource to the synthetic biology community for specifying genetic components that can be assembled through a scalable and modular architecture.

Phosphorylation is a post-translational modification that regulates the activity of several key proteins in bacteria and eukaryotes. Accordingly, a variety of tools has been developed to measure kinase activity. To couple phosphorylation to an in vivo fluorescent readout we used the Bacillus subtilis kinase PtkA, transmembrane activator TkmA and the repressor FatR to construct a genetic circuit in E. coli. By tuning the repressor and kinase expression level at the same time, we were able to show a 4.2-fold increase in signal upon kinase induction. We furthermore validated that the previously reported FatR Y45E mutation1 attenuates operator repression. This genetic circuit provides a starting point for computational protein design and a metagenomic library-screening tool.

Genetic memory enables the recording of information in the DNA of living cells. Memory can record a transient environmental signal or cell state that is then recalled at a later time. Permanent memory is implemented using irreversible recombinases that invert the orientation of a unit of DNA, corresponding to the [0,1] state of a bit. To expand the memory capacity, we have applied bioinformatics to identify 34 phage integrases (and their cognate attB and attP recognition sites), from which we build 11 memory switches that are perfectly orthogonal to each other and the FimE and HbiF bacterial invertases. Using these switches, a memory array is constructed in Escherichia coli that can record 1.375 bytes of information. It is demonstrated that the recombinases can be layered and used to permanently record the transient state of a transcriptional logic gate., United States. Defense Advanced Research Projects Agency (DARPA CLIO N66001-12-C-4016), United States. Defense Advanced Research Projects Agency (DARPA CLIO N66001-12-C-4018), United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-13-1-0074), National Institutes of Health (U.S.) (GM095765), National Institute of General Medical Sciences (U.S.) (P50 GM098792), National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (SynBERC EEC0540879), FA9550-11-C-0028, American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship (32 CFR 168a)

Methylating chemicals are common in industry and agriculture and are often toxic, partly due to their propensity to methylate DNA. The Escherichia coli Ada protein detects methylating compounds by sensing aberrant methyl adducts on the phosphoester backbone of DNA. We characterize this system as a genetic sensor and engineer it to lower the detection threshold. By overexpressing Ada from a plasmid, we improve the sensor’s dynamic range to 350-fold induction and lower its detection threshold to 40 μM for methyl iodide. In eukaryotes, there is no known sensor of methyl adducts on the phosphoester backbone of DNA. By fusing the N-terminal domain of Ada to the Gal4 transcriptional activation domain, we built a functional sensor for methyl phosphotriester adducts in Saccharomyces cerevisiae. This sensor can be tuned to variable specifications by altering the expression level of the chimeric sensor and changing the number of Ada operators upstream of the Gal4-sensitive reporter promoter. These changes result in a detection threshold of 28 μM and 5.2-fold induction in response to methyl iodide. When the yeast sensor is exposed to different SN1 and SN2 alkylating compounds, its response profile is similar to that observed for the native Ada protein in E. coli, indicating that its native function is retained in yeast. Finally, we demonstrate that the specifications achieved for the yeast sensor are suitable for detecting methylating compounds at relevant concentrations in environmental samples. This work demonstrates the movement of a sensor from a prokaryotic to eukaryotic system and its rational tuning to achieve desired specifications.

A wide range of biological processes exhibit circadian rhythm, enabling plants to adapt to the environmental day-night cycle. This rhythm is generated by the so-called 'circadian clock'. Although a number of genetic approaches have identified >25 clock-associated genes involved in the Arabidopsis clock mechanism, the molecular functions of a large part of these genes are not known. Recent comprehensive studies have revealed the molecular functions of several key clock-associated proteins. This progress has provided mechanistic insights into how key clock-associated proteins are integrated, and may help in understanding the essence of the clock's molecular mechanisms.

The majority of models describing the kinetic properties of a microorganism for a given substrate are unstructured and empirical. They are formulated in this manner so that the complex mechanism of cell growth is simplified. Herein, a novel approach for modelling microbial growth kinetics is proposed, linking biomass growth and substrate consumption rates to the gene regulatory programmes that control these processes. A dynamic model of the TOL (pWW0) plasmid of Pseudomonas putida mt-2 has been developed, describing the molecular interactions that lead to the transcription of the upper and meta operons, known to produce the enzymes for the oxidative catabolism of m-xylene. The genetic circuit model was combined with a growth kinetic model decoupling biomass growth and substrate consumption rates, which are expressed as independent functions of the rate-limiting enzymes produced by the operons. Estimation of model parameters and validation of the model's predictive capability were successfully performed in batch cultures of mt-2 fed with different concentrations of m-xylene, as confirmed by relative mRNA concentration measurements of the promoters encoded in TOL. The growth formation and substrate utilisation patterns could not be accurately described by traditional Monod-type models for a wide range of conditions, demonstrating the critical importance of gene regulation for the development of advanced models closely predicting complex bioprocesses. In contrast, the proposed strategy, which utilises quantitative information pertaining to upstream molecular events that control the production of rate-limiting enzymes, predicts the catabolism of a substrate and biomass formation and could be of central importance for the design of optimal bioprocesses.

We have investigated dynamics of synthetic genetic oscillators - repressilators - coupled through autoinducer diffusion. The model of the system with phase-repulsive coupling structure is under consideration. We have examined emergence of periodic regimes, stable inhomogeneous steady states depending on the main systems parameters: coupling strength and maximal transcription rate. It has been shown that autoinducer production module added to the isolated repressilator cause the limit cycle to disappear through infinite period bifurcation for sufficiently large transcription rate. We have found hysteresis of limit cycle and stable steady state the size of which is determined by ratio between mRNA and protein lifetimes. Two coupled oscillators system demonstrates stable anti-phase oscillations which can become a chaotic regime through invariant torus emergence or via Feigenbaum scenario.

Microbial engineering often requires fine control over protein expression--for example, to connect genetic circuits or control flux through a metabolic pathway. To circumvent the need for trial and error optimization, we developed a predictive method for designing synthetic ribosome binding sites, enabling a rational control over the protein expression level. Experimental validation of100 predictions in Escherichia coli showed that the method is accurate to within a factor of 2.3 over a range of 100,000-fold. The design method also correctly predicted that reusing identical ribosome binding site sequences in different genetic contexts can result in different protein expression levels. We demonstrate the method's utility by rationally optimizing protein expression to connect a genetic sensor to a synthetic circuit. The proposed forward engineering approach should accelerate the construction and systematic optimization of large genetic systems.

Background Understanding the mechanisms by which hundreds of diverse cell types develop from a single mammalian zygote has been a central challenge of developmental biology. Conrad H. Waddington, in his metaphoric “epigenetic landscape” visualized the early embryogenesis as a hierarchy of lineage bifurcations. In each bifurcation, a single progenitor cell type produces two different cell lineages. The tristable dynamical systems are used to model the lineage bifurcations. It is also shown that a genetic circuit consisting of two auto-activating transcription factors (TFs) with cross inhibitions can form a tristable dynamical system. Results We used gene expression profiles of pre-implantation mouse embryos at the single cell resolution to visualize the Waddington landscape of the early embryogenesis. For each lineage bifurcation we identified two clusters of TFs – rather than two single TFs as previously proposed – that had opposite expression patterns between the pair of bifurcated cell types. The regulatory circuitry among each pair of TF clusters resembled a genetic circuit of a pair of single TFs; it consisted of positive feedbacks among the TFs of the same cluster, and negative interactions among the members of the opposite clusters. Our analyses indicated that the tristable dynamical system of the two-cluster regulatory circuitry is more robust than the genetic circuit of two single TFs. Conclusions We propose that a modular hierarchy of regulatory circuits, each consisting of two mutually inhibiting and auto-activating TF clusters, can form hierarchical lineage bifurcations with improved safeguarding of critical early embryogenesis against biological perturbations. Furthermore, our computationally fast framework for modeling and visualizing the epigenetic landscape can be used to obtain insights from experimental data of development at the single cell resolution. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0169-8) contains supplementary material, which is available to authorized users.

Synthetic biology is a new field in which engineers, biologists, and chemists are working together to transform genetic engineering into an advanced engineering discipline, one in which the design and construction of novel genetic circuits are made possible through the application of engineering principles. This dissertation explores two engineering strategies to address the challenges of working with genetic technology, namely the development of standards for describing genetic components and circuits at separate yet connected levels of detail and the use of Genetic Design Automation (GDA) software tools to simplify and speed up the process of optimally designing genetic circuits. Its contributions to the field of synthetic biology include (1) a proposal for the next version of the Synthetic Biology Open Language (SBOL), an existing standard for specifying and exchanging genetic designs electronically, and (2) a GDA work ow that enables users of the software tool iBioSim to create an abstract functional specication, automatically select genetic components that satisfy the specication from a design library, and compose the selected components into a standardized genetic circuit design for subsequent analysis and physical construction. Ultimately, this dissertation demonstrates how existing techniques and concepts from electrical and computer engineering can be adapted to overcome the challenges of genetic design and is an example of what is possible when working with publicly available standards for genetic design.

Synthetic genetic systems share resources with the host, including machinery for transcription and translation. Phage RNA polymerases (RNAPs) decouple transcription from the host and generate high expression. However, they can exhibit toxicity and lack accessory proteins (σ factors and activators) that enable switching between different promoters and modulation of activity. Here, we show that T7 RNAP (883 amino acids) can be divided into four fragments that have to be co‐expressed to function. The DNA‐binding loop is encoded in a C‐terminal 285‐aa ‘σ fragment’, and fragments with different specificity can direct the remaining 601‐aa ‘core fragment’ to different promoters. Using these parts, we have built a resource allocator that sets the core fragment concentration, which is then shared by multiple σ fragments. Adjusting the concentration of the core fragment sets the maximum transcriptional capacity available to a synthetic system. Further, positive and negative regulation is implemented using a 67‐aa N‐terminal ‘α fragment’ and a null (inactivated) σ fragment, respectively. The α fragment can be fused to recombinant proteins to make promoters responsive to their levels. These parts provide a toolbox to allocate transcriptional resources via different schemes, which we demonstrate by building a system which adjusts promoter activity to compensate for the difference in copy number of two plasmids., United States. Office of Naval Research (N00014‐13‐1‐0074), National Institutes of Health (U.S.) (5R01GM095765), National Science Foundation (U.S.) (Synthetic Biology Engineering Research Center (SA5284‐11210)), United States. Dept. of Defense (National Defense Science and Engineering Graduate Fellowship (NDSEG) Program)), Hertz Foundation (Fellowship)

Background Because of the lack of an ideal promoter that selectively identifies cancer cells and robustly drives therapeutic gene expression, the conventional strategy for cancer gene therapy offers limited control of specificity and efficacy. A possible way to overcome these limitations is to construct logic circuits capable of integrating cellular information from multiple promoters as inputs. Methods Here, we designed and constructed a modular AND gate circuit based on CRISPR-cas9 system through combining the cancer-specific promoter of human telomerase reverse transcriptase (hTERT) gene with the bladder-specific promoter of human uroplakin II gene. The AND gate circuit integrates cellular information from the two promoters as inputs and activates the CMV promoter of output gene repressed by LacI only when both inputs are active in the tested cell lines. The integration occurs via an interaction between the Cas9 protein and sgRNA targeting LacI. Results Using the luciferase reporter as the output gene, we have shown that the circuit specifically detected bladder cancer cells and significantly enhanced luciferase expression in comparison to the hTERT-renilla luciferase (Rluc) construct. We also demonstrated the modularity of this circuit by replacing the output with other cellular functional genes including hBAX, p21, and E-cadherin. The circuit effectively inhibited bladder urothelial carcinoma cell growth, induced apoptosis, and decreased cell motility by regulating the corresponding gene. Conclusions This approach provides a synthetic biology platform for targeting and controlling bladder cancer.

Cells react to their environment through gene regulatory networks. Network integrity requires minimization of undesired crosstalk between their biomolecules. Similar constraints also limit the use of regulators when building synthetic circuits for engineering applications. Here, we mapped the promoter specificities of extracytoplasmic function (ECF) σs as well as the specificity of their interaction with anti‐σs. DNA synthesis was used to build 86 ECF σs (two from every subgroup), their promoters, and 62 anti‐σs identified from the genomes of diverse bacteria. A subset of 20 σs and promoters were found to be highly orthogonal to each other. This set can be increased by combining the −35 and −10 binding domains from different subgroups to build chimeras that target sequences unrepresented in any subgroup. The orthogonal σs, anti‐σs, and promoters were used to build synthetic genetic switches in Escherichia coli. This represents a genome‐scale resource of the properties of ECF σs and a resource for synthetic biology, where this set of well‐characterized regulatory parts will enable the construction of sophisticated gene expression programs., Life Technologies, Inc., United States. Defense Advanced Research Projects Agency (Chronicle of Lineage Indicative of Origins N66001-12-C-4018), United States. Office of Naval Research (N00014-10-1-0245), National Institutes of Health (U.S.) (NIH AI067699), National Science Foundation (U.S.). Synthetic Biology Engineering Research Center (SA5284-11210), American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship, Hertz Foundation (Fellowship)

Background The use of in silico simulations as a basis for designing artificial biological systems (and experiments to characterize them) is one of the tangible differences between Synthetic Biology and “classical” Genetic Engineering. To this end, synthetic biologists have adopted approaches originating from the traditionally non-biological fields of Nonlinear Dynamics and Systems & Control Theory. However, due to the complex molecular interactions affecting the emergent properties of biological systems, mechanistic descriptions of even the simplest genetic circuits (transcriptional feedback oscillators, bi-stable switches) produced by these methods tend to be either oversimplified, or numerically intractable. More comprehensive and realistic models can be approximated by constructing “toy” genetic circuits that provide the experimenter with some degree of control over the transcriptional dynamics, and allow for experimental set-ups that generate reliable data reflecting the intracellular biochemical state in real time. To this end, we designed two genetic circuits (basic and tunable) capable of exhibiting synchronized oscillatory green fluorescent protein (GFP) expression in small populations of Escherichia coli cells. The functionality of the basic circuit was verified microscopically. High-level visualizations of computational simulations were analyzed to determine whether the reliability and utility of a synchronized transcriptional oscillator could be enhanced by the introduction of chemically inducible repressors. Results Synchronized oscillations in GFP expression were repeatedly observed in chemically linked sub-populations of cells. Computational simulations predicted that the introduction of independently inducible repressors substantially broaden the range of conditions under which oscillations could occur, in addition to allowing the frequency of the oscillation to be tuned. Conclusions The genetic circuits described here may prove to be valuable research tools for the study of synchronized transcriptional feedback loops under a variety of conditions and experimental set-ups. We further demonstrate the benefit of using abstract visualizations to discover subtle non-linear trends in complex dynamic models with large parameter spaces.

Synthetic biology is an emerging field that combines molecular biology with engineering principles, which requires abstraction levels applied to a modular biological componentry. The Registry of Standard Biological Parts harbours such a repository of standardized parts, and thereby facilitates the combination of complex molecular modules to novel genetic circuits and devices. However, since finding the best parts for a pre-determined genetic design can be time consuming, we devised the Constructor, a web tool that recommends the smallest number of cloning steps for pre-designed circuits, and implements user-defined quality checks. We present the Constructor (http://www.systemsbiology.nl/the_constructor) as a constructive web tool that simplifies the in silico assembly of pre-designed gene circuitries from standard parts, reducing both planning and subsequent cloning time.

The complexity of the regulatory network and the interactions that occur in the intracellular environment of microorganisms highlight the importance in developing tractable mechanistic models of cellular functions and systematic approaches for modelling biological systems. To this end, the existing process systems engineering approaches can serve as a vehicle for understanding, integrating and designing biological systems and processes. Here, we review the application of a holistic approach for the development of mathematical models of biological systems, from the initial conception of the model to its final application in model-based control and optimisation. We also discuss the use of mechanistic models that account for gene regulation, in an attempt to advance the empirical expressions traditionally used to describe micro-organism growth kinetics, and we highlight current and future challenges in mathematical biology. The modelling research framework discussed herein could prove beneficial for the design of optimal bioprocesses, employing rational and feasible approaches towards the efficient production of chemicals and pharmaceuticals. 2012 Bernstein and Carlson.

The molecular and genetic events responsible for the growth kinetics of a microorganism can be extensively influenced by the presence of mixtures of substrates leading to unusual growth patterns, which cannot be accurately predicted by mathematical models developed using analogies to enzyme kinetics. Towards this end, we have combined a dynamic mathematical model of the Ps/Pr promoters of the TOL (pWW0) plasmid of Pseudomonas putida mt-2, involved in the metabolism of m-xylene, with the growth kinetics of the microorganism to predict the biodegradation of m-xylene and succinate in batch cultures. The substrate interactions observed in mixed-substrate experiments could not be accurately described by models without directly specifying the type of interaction even when accounting for enzymatic interactions. The structure of the genetic circuit–growth kinetic model was validated with batch cultures of mt-2 fed with m-xylene and succinate and its predictive capability was confirmed by successfully predicting independent sets of experimental data. Our combined genetic circuit–growth kinetic modelling approach exemplifies the critical importance of the molecular interactions of key genetic circuits in predicting unusual growth patterns. Such strategy is more suitable in describing bioprocess performance, which current models fail to predict.

Available to synthetic biologists are a wide range of genetic devices. Many of these devices are able to either sense or alter local conditions. The ability to sense a multitude of inputs combined with diverse outputs could enable engineered organisms that interact with their environment in new and complex ways. Currently the complexity of such systems has been limited by our ability to integrate several inputs into a desired output. Simple combinational logic functions, containing 1 to 3 logic gates, have been constructed in Escherichia coli, but more complex logic networks are needed to fully exploit the opportunities presented by these sensors and actuators. Use of genetic logic gates is constrained by the specific molecular interactions that are used to implement each gate. These interactions involve diffusible molecules that can move within the cytoplasm of the cell and therefore are not spatially separated from other gates. To make larger logic blocks, sets of gates that use unique molecular interactions with minimal crosstalk are required.Zinc finger proteins (ZFPs) can be used to predictably create a large number of unique protein-DNA interactions. These proteins can then be used to build transcriptional activators or repressors in E. coli, but these methods are not well defined. Attempts at using ZFPs to make one-hybrid transcriptional activators have failed to give a fold activation of higher than 2. ZFP repressors based on steric hindrance of RNA polymerase performed better with fold repressions values of up to 300. The positional dependence of the ZFP operator site within the promoter was investigated, and both position and dissociation constant were found to play important roles in determining the level of repression. ZFP based repressors without cooperativity cannot be used to create logic gates. A new inverter topology using both ZFP based repressors and sRNA was designed. This topology uses a reference promoter to set the switching threshold of the gate. There are no cooperative interactions in this topology, but the maximum slope of the transfer function is similar to a Hill-equation with a coefficient of 10. The high slope and excellent transfer function of these gates make them robust to many types of parameter variation and noise.A set of 27 validated ZFP repressors and 27 promoters with ZFP operator sites were created and tested for non-orthogonal interactions. A sub-set of 5 repressor-promoter pairs were found to have a high degree of orthogonality where the cognate pairs resulted in more than 73% attenuation of the promoter and non-cognate pairs gave less than 19% attenuation. The ZFPs and promoter used in this task were far from optimal and these attenuation values could readily be improved.The combination of these orthogonal repressor-promoter pairs and the new logic gate topology should enable more logic gates to be implemented in a single E. coli cell.

A novel modeling approach for the description of bioprocesses is proposed, linking microbial growth kinetics to gene regulation. An example is given with the development and experimental validation of a dynamic mathematical model of the TOL plasmid of Pseudomonas putida mt-2, which is used for the metabolism of m-xylene. The model of this genetic circuit is coupled to a growth kinetic model through predictions of rate-limiting enzyme concentrations that control biomass growth and substrate consumption. Batch cultures of mt-2 fed with m-xylene were performed to estimate model parameters and to confirm that the combined model successfully describes the bioprocess, through mRNA, biomass and m-xylene concentration measurements. However, mathematical models developed exclusively based on macroscopic measurements failed to predict the process variables, highlighting the importance of gene regulation for the development of advanced biological models. © 2011 Elsevier B.V.

The type III secretion system (T3SS) exports proteins from the cytoplasm, through both the inner and outer membranes, to the external environment. Here, a system is constructed to harness the T3SS encoded within Salmonella Pathogeneity Island 1 (SPI-1) to export proteins of biotechnological interest. The system is composed of an operon containing the target protein fused to an N-terminal secretion tag and its cognate chaperone. Transcription is controlled by a genetic circuit that only turns on when the cell is actively secreting protein. The system is refined using a small human protein (DH domain) and demonstrated by exporting an array of spider silk monomers representative of different types of spider silk. Synthetic genes encoding silk monomers were designed to enhance genetic stability and codon usage, constructed by automated DNA synthesis, and cloned into the secretion control system. Secretion rates up to 1.8 mg L-1 hour-1 are demonstrated with up to 14% of expressed protein secreted. This work introduces new parts to control protein secretion in gram negative bacteria, which will be broadly applicable to problems in biotechnology.

A modelling framework that consists of model building, validation and analysis, leading to model-based design of experiments and to the application of optimisation-based model-predictive control strategies for the development of optimised bioprocesses is presented. An example of this framework is given with the construction and experimental validation of a dynamic mathematical model of the Ps/Pr promoters system of the TOL plasmid, which is used for the metabolism of m -xylene by Pseudomonas putida mt-2. Furthermore, the genetic circuit model is combined with the growth kinetics of the strain in batch cultures, demonstrating how the description of key genetic circuits can facilitate the improvement of existing growth kinetic models that fail to predict unusual growth patterns. Consequently, the dynamic model is combined with global sensitivity analysis, which is used to identify the presence of significant model parameters, constituting a model-based methodology for the formulation of genetic circuit optimization methods.

In this paper, we apply a rigorous quasi-steady state approximation method on a family of models describing a gene regulated by a polymer of its own protein. We study the absence of oscillations for this family of models and prove that Poincare-Andronov-Hopf bifurcations arise if and only if the number of polymerizations is greater than 8. A result presented in a former paper at Algebraic Biology 2007is thereby generalized. The rigorous method is illustrated over the basic enzymatic reaction.

Microorganisms use genetic circuits to integrate environmental information. We have constructed a synthetic AND gate in the bacterium Escherichia coli that integrates information from two promoters as inputs and activates a promoter output only when both input promoters are transcriptionally active. The integration occurs via an interaction between an mRNA and tRNA. The first promoter controls the transcription of a T7 RNA polymerase gene with two internal amber stop codons blocking translation. The second promoter controls the amber suppressor tRNA supD. When both components are transcribed, T7 RNA polymerase is synthesized and this in turn activates a T7 promoter. Because inputs and outputs are promoters, the design is modular; that is, it can be reconnected to integrate different input signals and the output can be used to drive different cellular responses. We demonstrate this modularity by wiring the gate to integrate natural promoters (responding to Mg(2+) and AI-1) and using it to implement a phenotypic output (invasion of mammalian cells). A mathematical model of the transfer function is derived and parameterized using experimental data.

In this paper we develop a new mathematical approach to the pattern formation problem in biology. This problem was first posed mathematically by A.M. Turing, however some principal questions were left open (for example, whether there exists a “universal” mathematical model that allows one to obtain any spatio-temporal patterns). Here we consider the pattern formation ability of some class of genetic circuits. First, we show that the genetic circuits are capable of generating arbitrary spatio-temporal patterns. Second, we give upper and lower bounds on the number of genes in a circuit generating a given pattern. A connection between the complexity of gene interaction and the pattern complexity is found. We investigate the stochastic stability of patterning algorithms. Results are consistent with experimental data.

Background In order to understand and regulate complex genetic networks in living cells, it is important to build simple and well-defined genetic circuits. We designed such circuits using a synthetic biology approach that included mathematical modeling and simulation, with a focus on the effects by which downstream reporter genes are involved in the regulation of synthetic genetic circuits. Results Our results indicated that downstream genes exert two main effects on genes involved in the regulation of synthetic genetic circuits: (1) competition for regulatory proteins and (2) protein degradation in the cell. Conclusions Our findings regarding the effects of downstream genes on regulatory genes and the role of impedance in driving large-scale and complex genetic circuits may facilitate the design of more accurate genetic circuits. This design will have wide applications in future studies of systems and synthetic biology.

A wide variety of approaches, ranging from Petri nets to systems of partial differential equations, have been used to model very specific aspects of cellular or biochemical functions. Here we describe how an agent-based or dynamic cellular automata (DCA) approach can be used as a very simple, yet very general method to model many different kinds of cellular or biochemical processes. Specifically, using simple pair wise interaction rules coupled with random object moves to simulate Brownian motion, we show how the DCA approach can be used to easily and accurately model diffusion, viscous drag, enzyme rate processes, metabolism (the Kreb's cycle), and complex genetic circuits (the repressilator). We also demonstrate how DCA approaches are able to accurately capture the stochasticity of many biological processes. The success and simplicity of this technique suggests that many other physical properties and significantly more complicated aspects of cellular behavior could be modeled using DCA methods. An easy-to-use, graphically-based computer program, called SimCell, was developed to perform the DCA simulations described here. It is available at http://wishart.biology.ualberta.ca/SimCell/.