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Cell signalling and communication are fundamental to living cellular communities. For the past two decades, there has been continuous development of bottom-up engineered synthetic cells, which have become more and more similar to their natural counterparts. However, we are only scratching the surface with the development of synthetic cellular communities and their integration into natural tissues. Here, we review different intercellular communication mechanisms engineered for synthetic cells and classify them based on their resemblance to natural cell signalling mechanisms: autocrine, paracrine, and juxtacrine. In particular, we highlight recent advances in molecular tools for intercellular communication designs and discuss potential applications of engineering synthetic cellular communities and synthetic cell-natural cell communication. With further advances in this area, synthetic cellular communities will be powerful tools for understanding and manipulating cellular functions, thus unlocking potential applications in biosensing, cellular reprogramming, and sustainability. 

We consider the well-studied problem of completing a rank- , -incoherent matrix from incomplete observations. We focus on this problem in the semi-random setting where each entry is independently revealed with probability at least . Whereas multiple nearly-linear time algorithms have been established in the more specialized fully-random setting where each entry is revealed with probablity exactly , the only known nearly-linear time algorithm in the semi-random setting is due to [CG18], whose sample complexity has a polynomial dependence on the inverse accuracy and condition number and thus cannot achieve high-accuracy recovery. Our main result is the first high-accuracy nearly-linear time algorithm for solving semi-random matrix completion, and an extension to the noisy observation setting. Our result builds upon the recent short-flat decomposition framework of [KLLST23a, KLLST23b] and leverages fast algorithms for flow problems on graphs to solve adaptive reweighting subproblems efficiently 

The authors provide the first tight sample complexity bounds for shadow tomography and classical shadows in the regime where the target error is below some sufficiently small inverse polynomial in the dimension of the Hilbert space. Specifically, they present a protocol that, given any 𝑚 ∈ 𝑁 m∈N and 𝜖 ≤ 𝑂 ( 𝑑 − 1 / 2 ) ϵ≤O(d −1/2 ), measures 𝑂 ( log ⁡ ( 𝑚 ) / 𝜖 2 ) O(log(m)/ϵ 2 ) copies of an unknown mixed state 𝜌 ∈ 𝐶 𝑑 × 𝑑 ρ∈C d×d and outputs a classical description of 𝜌 ρ. This description can then be used to estimate any collection of 𝑚 m observables to within additive accuracy 𝜖 ϵ. Previously, even for the simpler case of shadow tomography where observables are known in advance, the best known rates either scaled benignly but suboptimally in all of 𝑚 , 𝑑 , 𝜖 m,d,ϵ, or scaled optimally in 𝜖 , 𝑚 ϵ,m but included additional polynomial factors in 𝑑 d. Interestingly, the authors also show via dimensionality reduction that one can rescale 𝜖 ϵ and 𝑑 d to reduce to the regime where 𝜖 ≤ 𝑂 ( 𝑑 − 1 / 2 ) ϵ≤O(d −1/2 ). Their algorithm draws on representation-theoretic tools developed in the context of full state tomography. 

Cell-free expression (CFE) systems are powerful tools in synthetic biology that allow biomimicry of cellular functions like biosensing and energy regeneration in synthetic cells. Reconstruction of a wide range of cellular processes, however, requires successful reconstitution of membrane proteins into the membrane of synthetic cells. While expression of soluble proteins is usually successful in common CFE systems, reconstitution of membrane proteins in lipid bilayers of synthetic cells has proven to be challenging. Here, a method for reconstitution of a model membrane protein, bacterial glutamate receptor (GluR0), in giant unilamellar vesicles (GUVs) as model synthetic cells based on encapsulation and incubation of the CFE reaction inside synthetic cells is demonstrated. Utilizing this platform, the effect of substituting N-terminal signal peptide of GluR0 with proteorhodopsin signal peptide on successful co-translational translocation of GluR0 into membranes of hybrid GUVs is demonstrated. This method provides a robust procedure that will allow cell-free reconstitution of various membrane proteins in synthetic cells. 

Constructing molecular classifiers that enable cells to recognize linear and non-linear input patterns would expand the biocomputational capabilities of engineered cells, thereby unlocking their potential in diagnostics and therapeutic applications. While several biomolecular classifier schemes have been designed, the effect of biological constraints such as resource limitation and competitive binding on the function of those classifiers has been left unexplored. Here, we first demonstrate the design of a sigma factor-based perceptron as a molecular classifier working on the principles of molecular sequestration between the sigma factor and its anti-sigma molecule. We then investigate how the output of the biomolecular perceptron,i.e., its response pattern or decision boundary, is affected by the competitive binding of sigma factors to a pool of shared and limited resources of core RNA polymerase. Finally, we reveal the influence of sharing limited resources on multi-layer perceptron neural networks and outline design principles that enable the construction of non-linear classifiers using sigma-based biomolecular neural networks in the presence of competitive resource-sharing effects. 

Cell signaling through direct physical cell-cell contacts plays vital roles in biology during development, angiogenesis, and immune response. Intercellular communication mechanisms between synthetic cells constructed from the bottom up are majorly reliant on diffusible chemical signals, thus limiting the range of responses in receiver cells. Engineering contact-dependent signaling between synthetic cells promises to unlock more complicated signaling schemes with different types of responses. Here, we design and demonstrate a light-activated contact-dependent communication tool for synthetic cells. We utilize a split bioluminescent protein to limit signal generation exclusively to contact interfaces of synthetic cells, driving the recruitment of a photoswitchable protein in receiver cells, akin to juxtacrine signaling in living cells. Our modular design not only demonstrates contact-dependent communication between synthetic cells but also provides a platform for engineering orthogonal contact-dependent signaling mechanisms.