Supplementary data are available at Bioinformatics online.

Growth and differentiation of multicellular systems is orchestrated by spatially restricted gene expression programs in specialized subpopulations. The targeted manipulation of such processes by synthetic tools with high-spatiotemporal resolution could, therefore, enable a deepened understanding of developmental processes and open new opportunities in tissue engineering. Here, we describe the first red/far-red light-triggered gene switch for mammalian cells for achieving gene expression control in time and space. We show that the system can reversibly be toggled between stable on- and off-states using short light pulses at 660 or 740 nm. Red light-induced gene expression was shown to correlate with the applied photon number and was compatible with different mammalian cell lines, including human primary cells. The light-induced expression kinetics were quantitatively analyzed by a mathematical model. We apply the system for the spatially controlled engineering of angiogenesis in chicken embryos. The system’s performance combined with cell- and tissue-compatible regulating red light will enable unprecedented spatiotemporally controlled molecular interventions in mammalian cells, tissues and organisms.

The emergence and future of mammalian synthetic biology depends on technologies for orchestrating and custom tailoring complementary gene expression and signaling processes in a predictable manner. Here, we demonstrate for the first time multi-chromatic expression control in mammalian cells by differentially inducing up to three genes in a single cell culture in response to light of different wavelengths. To this end, we developed an ultraviolet B (UVB)-inducible expression system by designing a UVB-responsive split transcription factor based on the Arabidopsis thaliana UVB receptor UVR8 and the WD40 domain of COP1. The system allowed high (up to 800-fold) UVB-induced gene expression in human, monkey, hamster and mouse cells. Based on a quantitative model, we determined critical system parameters. By combining this UVB-responsive system with blue and red light-inducible gene control technology, we demonstrate multi-chromatic multi-gene control by differentially expressing three genes in a single cell culture in mammalian cells, and we apply this system for the multi-chromatic control of angiogenic signaling processes. This portfolio of optogenetic tools enables the design and implementation of synthetic biological networks showing unmatched spatiotemporal precision for future research and biomedical applications.

The mechanical properties of the extracellular environment govern key cellular decision-making processes such as proliferation, differentiation, or migration. [1] Thus, analyzing how cells gauge and interact with their mechanical environment is critical not only for understanding physiological and pathological processes but also for engineering cell and tissue growth and differentiation in regenerative medicine. [2] Although studies using passive elastic or viscoelastic materials have revealed valuable information about cell-matrix interactions, matrices with adjustable mechanical properties more closely reflect the dynamic environments many cells are exposed to in a living organism. [3] In order to recapitulate these dynamic environments, several materials have been developed, which enable the Interrogation and control of cellular fate and function using optogenetics is providing revolutionary insights into biology. Optogenetic control of cells is achieved by coupling genetically encoded photoreceptors to cellular effectors and enables unprecedented spatiotemporal control of signaling processes. Here, a fast and reversibly switchable photoreceptor is used to tune the mechanical properties of polymer materials in a fully reversible, wavelengthspecific, and dose-and space-controlled manner. By integrating engineered cyanobacterial phytochrome 1 into a poly(ethylene glycol) matrix, hydrogel materials responsive to light in the cell-compatible red/far-red spectrum are synthesized. These materials are applied to study in human mesenchymal stem cells how different mechanosignaling pathways respond to changing mechanical environments and to control the migration of primary immune cells in 3D. This optogenetics-inspired matrix allows fundamental questions of how cells react to dynamic mechanical environments to be addressed. Further, remote control of such matrices can create new opportunities for tissue engineering or provide a basis for optically stimulated drug depots. BiomaterialsThe ORCID identification number(s) for the author(s) of this article can be found under https://doi.

In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined parameter estimates and predictions. Too small of a model will not be able to describe the data whereas a model which is too large tends to overfit measurement errors and does not provide precise predictions. Typically, the model is modified and tuned to fit the data, which often results in an oversized model. To restore the balance between model complexity and available measurements, either new data has to be gathered or the model has to be reduced. In this manuscript, we present a data-based method for reducing non-linear models. The profile likelihood is utilised to assess parameter identifiability and designate likely candidates for reduction. Parameter dependencies are analysed along profiles, providing context-dependent suggestions for the type of reduction. We discriminate four distinct scenarios, each associated with a specific model reduction strategy. Iterating the presented procedure eventually results in an identifiable model, which is capable of generating precise and testable predictions. Source code for all toy examples is provided within the freely available, open-source modelling environment Data2Dynamics based on MATLAB available at http://www.data2dynamics.org/, as well as the R packages dMod/cOde available at https://github.com/dkaschek/. Moreover, the concept is generally applicable and can readily be used with any software capable of calculating the profile likelihood.