Frontiers in systems biology最新文献

Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and signaling. Crucially, these studies reveal that cellular heterogeneity is not a result of randomness or a failure in cellular control systems, but instead is a predictable aspect of multicellular systems. We hypothesize that individual cells in complex tissues can behave as reward-maximizing agents and that differences in reward perception can explain heterogeneity. In this perspective, we introduce inverse reinforcement learning as a novel approach for analyzing cellular heterogeneity. We briefly detail experimental approaches for measuring cellular heterogeneity over time and how these experiments can generate datasets consisting of cellular states and actions. Next, we show how inverse reinforcement learning can be applied to these datasets to infer how individual cells choose different actions based on heterogeneous states. Finally, we introduce potential applications of inverse reinforcement learning to three cell biology problems. Overall, we expect inverse reinforcement learning to reveal why cells behave heterogeneously and enable identification of novel treatments based on this new understanding.

A rich pipeline of therapeutic candidates is advancing for Parkinson's disease, many of which are targeting the underlying pathophysiology of disease. Emerging evidence grounded in novel genetics and biomarker discoveries is illuminating the true promise of precision medicine-based therapeutic strategies for PD. There has been a growing effort to investigate disease-modifying therapies by designing clinical trials for genetic forms of PD - providing a clearer link to underlying pathophysiology. Leading candidate genes based on human genetic findings that are under active investigation in an array of basic and translational models include SNCA, LRRK2, and GBA. Broad investigations across mechanistic models show that these genes signal through common molecular pathways, namely, autosomal lysosomal pathways, inflammation and mitochondrial function. Therapeutic clinical trials to date based on genetically defined targets have not yet achieved approvals; however, much is to be learned from such pioneering trials. Fundamental principles of drug development that include proof of pharmacology in target tissue are critical to have confidence in advancing such precision-based therapies. There is a clear need for downstream biomarkers of leading candidate therapies to demonstrate proof of mechanism. The current regulatory landscape is poised and primed to support translational modeling strategies for the effective advancement of PD disease-modifying therapeutic candidates. A convergence of rich complex data that is available, the regulatory framework of model informed drug development (MIDD), and the new biological integrated staging frameworks when combined are collectively setting the stage for advancing new approaches in PD to accelerate progress. This perspective review highlights the potential of quantitative systems pharmacology (QSP) modeling in contributing to the field and hastening the pace of progress in advancing collaborative approaches for urgently needed PD disease-modifying treatments.

The recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable, the CRNN acts as a digital twin of a classical chemical reaction network. In this study, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together, this provides a new framework for robust, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems.