SCNS takes single-cell qPCR or RNA-sequencing data, and treats expression profiles as binary states, where a value of 1 indicates a gene is expressed and 0 indicates that it is not. This mechanistic model explains the molecular changes underlying the biological process. SCNS is a tool for understanding differentiation, developmental, or reprogramming journeys, reconstructing models from single-cell data taken across a time course. The tool is controlled via a web-based graphical interface.
We have now developed the Single Cell Network Synthesis tool (SCNS) into a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. We previously introduced an approach for synthesising executable gene regulatory network models directly from single-cell gene expression time course data sets, without the need of prior knowledge of the topology of the network or a detailed specification of its behaviour, which for many systems does not exist.
However, these models were manually curated and are the result of knowledge of the structure of the network built up over decades of laboratory experimentation. Successful examples of executable modelling include the Boolean network models of sea-urchin development and of blood stem cells. An executable model can also be used to obtain a global dynamic picture of how the system responds to various perturbations. These models are amendable to the use of state space analysis and model checking algorithms to analyse all of the many possible executions of the model and generate new predictions that can be tested experimentally. Executable models capture essential qualitative details of a biological process and aim to mimic the order of events and the long-term behaviour of the system. SCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.Įxecutable gene regulatory network models have been successfully built and used to obtain a better mechanistic understanding of developmental, homeostatic and diseased cellular decision making processes. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. The Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. Reconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes.
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