Optogenetic control of gene expression in plants in the presence of ambient white light.

Optogenetics is the genetic approach for controlling cellular processes with light. It provides spatiotemporal, quantitative and reversible control over biological signaling and metabolic processes, overcoming limitations of chemically inducible systems. However, optogenetics lags in plant research because ambient light required for growth leads to undesired system activation. We solved this issue by developing plant usable light-switch elements (PULSE), an optogenetic tool for reversibly controlling gene expression in plants under ambient light. PULSE combines a blue-light-regulated repressor with a red-light-inducible switch. Gene expression is only activated under red light and remains inactive under white light or in darkness. Supported by a quantitative mathematical model, we characterized PULSE in protoplasts and achieved high induction rates, and we combined it with CRISPR-Cas9-based technologies to target synthetic signaling and developmental pathways. We applied PULSE to control immune responses in plant leaves and generated Arabidopsis transgenic plants. PULSE opens broad experimental avenues in plant research and biotechnology.

PEtab-Interoperable specification of parameter estimation problems in systems biology.

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been-so far-no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.

Engineering a material-genetic interface as safety switch for embedded therapeutic cells.

Encapsulated cell-based therapies involve the use of genetically-modified cells embedded in a material in order to produce a therapeutic agent in a specific location in the patient's body. This approach has shown great potential in animal model systems for treating diseases such as type I diabetes or cancer, with selected approaches having been tested in clinical trials. Despite the promise shown by encapsulated cell therapy, though, there are safety concerns yet to be addressed, such as the escape of the engineered cells from the encapsulation material and the resulting production of therapeutic agents at uncontrolled sites in the body. For that reason, there is great interest in the implementation of safety switches that protect from those side effects. Here, we develop a material-genetic interface as safety switch for engineered mammalian cells embedded into hydrogels. Our switch allows the therapeutic cells to sense whether they are embedded in the hydrogel by means of a synthetic receptor and signaling cascade that link transgene expression to the presence of an intact embedding material. The system design is highly modular, allowing its flexible adaptation to other cell types and embedding materials. This autonomously acting switch constitutes an advantage over previously described safety switches, which rely on user-triggered signals to modulate activity or survival of the implanted cells. We envision that the concept developed here will advance the safety of cell therapies and facilitate their translation to clinical evaluation.

Context is key: normalization as a novel approach to sport specific preprocessing of KPI's for match analysis in soccer.

Key Performance Indicators (KPIs) have been investigated, validated and applied in multitude of sports for recruiting, coaching, opponent, self-analysis etc. Although a wide variety of in game performance indicators have been used as KPIs, they lack sports specific context. With the introduction of artificial intelligence and machine learning (AI/ML) in sports, the need for building intrinsic context into the independent variables is even greater as AI/ML models seem to perform better in terms of predictability but lack interpretability. The study proposes domain specific feature preprocessing method (normalization) that can be utilized across a wide range of sports and demonstrates its value through a specific data transformation by using team possession as a normalizing factor while analyzing defensive performance in soccer. The study performed two linear regressions and three gradient boosting machine models to demonstrate the value of normalization while predicting defensive performance. The results demonstrate that the direction of correlation of the relevant variables changes post normalization while predicting defensive performance of teams for the whole season. Both raw and normalized KPIs showing significant correlation with defensive performance (p < 0.001). The addition of the normalized variables contributes towards higher information gain, improved performance and increased interpretability of the models.

Artificial Intelligence Based Body Sensor Network Framework-Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare.

With the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality 'big data' in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

Liquid-liquid phase separation of light-inducible transcription factors increases transcription activation in mammalian cells and mice.

Light-inducible gene switches represent a key strategy for the precise manipulation of cellular events in fundamental and applied research. However, the performance of widely used gene switches is limited due to low tissue penetrance and possible phototoxicity of the light stimulus. To overcome these limitations, we engineer optogenetic synthetic transcription factors to undergo liquid-liquid phase separation in close spatial proximity to promoters. Phase separation of constitutive and optogenetic synthetic transcription factors was achieved by incorporation of intrinsically disordered regions. Supported by a quantitative mathematical model, we demonstrate that engineered transcription factor droplets form at target promoters and increase gene expression up to fivefold. This increase in performance was observed in multiple mammalian cells lines as well as in mice following in situ transfection. The results of this work suggest that the introduction of intrinsically disordered domains is a simple yet effective means to boost synthetic transcription factor activity.