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Multiplexor 2 to 1 a Truth table of the 2 to 1 multiplexer. b Spatial pattern of cells encoding the multiplexer device with their corresponding distances. M⁺aTc and M⁻aTc are, respectively, positive and negative modulatory cells responding to aTc. S2 cells produce AHL upon arabinose induction and S3 cells produce AHL upon rhamnose induction. CR are the reporter cells. c Multiplexer response upon input of the indicated combinations. Input concentrations are Ara = 10⁻³ M, aTc = 10⁻⁶ M and Rham = 1.5%. Error bars are the standard deviation (SD) of three independent experiments. Data are presented as mean values ± SD. The average fold change has been obtained from the mean of ON and OFF states from each input combination. Multiplexor 2 to 1 fold change: 6.76x. Source data are provided as a Source Data file.
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Much effort has been expended on building cellular computational devices for different applications. Despite the significant advances, there are still several addressable restraints to achieve the necessary technological transference. These improvements will ease the development of end-user applications working out of the lab. In this study, we pro...
Citations
... In this work, we combine simple inducer activation functions with spatial structure to produce modular, easily programmable bacterial computers. In contrast to previous work [16][17][18] , much of the information processing is encoded within interacting morphogen gradients, ...
... In comparison with previous, related approaches [16][17][18] our system allows the construction of any Boolean function with less biological complexity. This is because our choice of activation functions drastically offloads complexity from the internal cell dynamics to the morphogen field. ...
Biological computing is a promising field with potential applications in biosafety, environmental monitoring, and personalized medicine. Here we present work on the design of bacterial computers using spatial patterning to process information in the form of diffusible morphogen-like signals. We demonstrate, mathematically and experimentally, that single, modular, colonies can perform simple digital logic, and that complex functions can be built by combining multiple colonies, removing the need for further genetic engineering. We extend our experimental system to incorporate sender colonies as morphogen sources, demonstrating how one might integrate different biochemical inputs. Our approach will open up ways to perform biological computation, with applications in bioengineering, biomaterials and biosensing. Ultimately, these computational bacterial communities will help us explore information processing in natural biological systems.
... Moreover, synthetic biologists can take advantage of other inherent features of multicellular systems, such as the capacity for parallel and distributed computing, robustness to failure, modularity, and scalability [30,34,37]. Currently, multicellular systems have been designed that compute complex Boolean functions [28,[38][39][40][41][42][43][44], act as memory devices [45], behave as glucose sensors [46], generate specified spatial patterns [47], and recognize spatial patterns [48] among other functions [49]. ...
... Diffusible signaling molecules have also been used to form synthetic multicellular networks for ecological functions [37] (e.g. glucose-sensitive insulin production [46]) as well as biocomputing ([35], e.g. as digital circuits [39,40]). In reservoir computing, previous research has successfully applied neuronal communities with neuroelectric communication schemes as reservoirs. ...
The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions-computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.
... As the information molecules degrade during the propagation and cell signal processing, we increase the duration of the rectangular inputs to 30 mins. We also increase all the production rates by 40 folds, which can be achieved by amplifying circuits in practice [38]. For the thresholding unit, we set the thresholding parameters as C B 2 Th (t) = 0.7nM, C B 1 Th (t) = 0.45nM, and C B 0 Th (t) = 0.1nM. ...
... For the thresholding unit, we set the thresholding parameters as C B 2 Th (t) = 0.7nM, C B 1 Th (t) = 0.45nM, and C B 0 Th (t) = 0.1nM. Fig. 11 plots the simulation results of our proposed QCSK transmitter and the analytical results calculated by (38). We observe that the QCSK transmitter can modulate two bits into four different concentration levels, thus achieving QCSK modulation. ...
The design and engineering of molecular communication (MC) components capable of processing chemical concentration signals is the key to unleashing the potential of MC for interdisciplinary applications. By controlling the signaling pathway and molecule exchange between cell devices, synthetic biology provides the MC community with tools and techniques to achieve various signal processing functions. In this paper, we propose a design framework to realize any order concentration shift keying (CSK) systems based on simple and reusable single-input single-output cells. The design framework also exploits the distributed multicellular consortia with spatial segregation, which has advantages in system scalability, low genetic manipulation, and signal orthogonality. We also create a small library of reusable engineered cells and apply them to implement binary CSK (BCSK) and quadruple CSK (QCSK) systems to demonstrate the feasibility of our proposed design framework. Importantly, we establish a mathematical framework to theoretically characterize our proposed distributed multicellular systems. Specially, we divide a system into fundamental building blocks, from which we derive the impulse response of each block and the cascade of the impulse responses leads to the end-to-end response of the system. Simulation results obtained from the agent-based simulator BSim not only validate our CSK design framework but also demonstrate the accuracy of the proposed mathematical analysis.
... The enhancement of cell-to-cell communications and the efficiency of taxis are related to the realization of computational procedures inside the cells [70,83,84]. Today, the most common approach is based on genetic logic circuits [13,64]. ...
... Based on the central dogma of molecular biology, this method operates with transcription factors, RNA-switches, and other components to realize logic gates with output in the form of specific protein synthesis. The number of logic gates in the single cell can be limited because of the usage of cellular recourse (amino acids, energy intermediates, etc.), but computation can be separated between different cells with chemical communications for signal transfer [83,84]. Thus, combinations of cells with different goals can be discussed for some applications [61]. ...
The presented review focused on the microbial cell based system. This approach is based on the application of microorganisms as the main part of a robot that is responsible for the motility, cargo shipping, and in some cases, the production of useful chemicals. Living cells in such microrobots have both advantages and disadvantages. Regarding the advantages, it is necessary to mention the motility of cells, which can be natural chemotaxis or phototaxis, depending on the organism. There are approaches to make cells magnetotactic by adding nanoparticles to their surface. Today, the results of the development of such microrobots have been widely discussed. It has been shown that there is a possibility of combining different types of taxis to enhance the control level of the microrobots based on the microorganisms' cells and the efficiency of the solving task. Another advantage is the possibility of applying the whole potential of synthetic biology to make the behavior of the cells more controllable and complex. Biosynthesis of the cargo, advanced sensing, on/off switches, and other promising approaches are discussed within the context of the application for the microrobots. Thus, a synthetic biology application offers significant perspectives on microbial cell based microrobot development. Disadvantages that follow from the nature of microbial cells such as the number of external factors influence the cells, potential immune reaction, etc. They provide several limitations in the application, but do not decrease the bright perspectives of microrobots based on the cells of the microorganisms.
... Moreover, synthetic biologists can take advantage of other inherent features of multicellular systems, such as the capacity for parallel and distributed computing, robustness to failure, modularity, and scalability (28,29,31). Currently, multicellular systems have been designed that compute complex Boolean functions (26,(32)(33)(34)(35)(36)(37), act as memory devices (38), behave as glucose sensors (39), generate specified spatial patterns (40), and recognize spatial patterns (41) among other functions (42). ...
A bstract
The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate two benchmark signal processing tasks, computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.
... The standard mathematical proof that any Boolean function can be decomposed into a double summation of IDENTITY and NOT logics was used to build multicellular circuits encoding the IDENTITY and NOT logic into cells and then performing sums by mixing cell cultures together [54]. A subsequent paper simplified the implementation of digital functions by printing multiple cell populations onto branched paper devices using 'cellular ink' [55]. It was shown that these devices could be stored for up to ten days and still be effective. ...
... The relative complexity of our approach was assessed by comparison with two other implementations of spatially distributed biocomputers [54,55]. The first comparison is with an approach based on the separation of cells into a set of connected growth chambers [54]. ...
... The maximum number of independent growth chambers, named modules, was used for the comparison. A subsequent paper implemented similar circuits on branched 2D circuits on pieces of paper [55] and the maximum number of branches was used for comparison. These two measures were compared with the maximum total number of colonies required for any given ...
The design and construction of engineered biological systems has made great strides over the last few decades and a growing part of this is the application of mathematical and computational techniques to problems in synthetic biology. The use of distributed systems, in which an overall function is divided across multiple populations of cells, has the potential to increase the complexity of the systems we can build and overcome metabolic limitations. However, constructing biological distributed systems comes with its own set of challenges. In this thesis I present new tools for the design and control of distributed systems in synthetic biology. The first part of this thesis focuses on biological computers. I develop novel design algorithms for distributed digital and analogue computers composed of spatial patterns of communicating bacterial colonies. I prove mathematically that we can program arbitrary digital functions and develop an algorithm for the automated design of optimal spatial circuits. Furthermore, I show that bacterial neural networks can be built using our system and develop efficient design tools to do so. I verify these results using computational simulations. This work shows that we can build distributed biological computers using communicating bacterial colonies and different design tools can be used to program digital and analogue functions. The second part of this thesis utilises a technique from artificial intelligence, reinforcement learning, in first the control and then the understanding of biological systems. First, I show the potential utility of reinforcement learning to control and optimise interacting communities of microbes that produce a biomolecule. Second, I apply reinforcement learning to the design of optimal characterisation experiments within synthetic biology. This work shows that methods utilising reinforcement learning show promise for complex distributed bioprocessing in industry and the design of optimal experiments throughout biology.
The design and engineering of molecular communication (MC) components capable of processing chemical concentration signals is the key to unleashing the potential of MC for interdisciplinary applications. By controlling the signaling pathway and molecule exchange between cell devices, synthetic biology provides the MC community with tools and techniques to achieve various signal processing functions. In this paper, we propose a design framework to realize any order concentration shift keying (CSK) systems based on simple and reusable single-input single-output cells. The design framework also exploits the distributed computation on multicellular consortia with spatial segregation, which has advantages in system scalability, low genetic manipulation, and signal orthogonality. We also create a small library of simple logic engineered cells and apply them to implement binary CSK (BCSK) and quadruple CSK (QCSK) systems to demonstrate the feasibility of our proposed design framework. The simplicity of our engineered cells allows for their reuse in other systems beyond CSK. Importantly, we establish a mathematical framework to theoretically characterize our proposed distributed multicellular systems. Specially, we divide a system into fundamental building blocks, from which we derive the impulse response of each block and the cascade of the impulse responses leads to the end-to-end response of the system. Simulation results obtained from the agent-based simulator BSim not only validate our CSK design framework but also demonstrate the accuracy of the proposed mathematical analysis.
