Descripción del proyecto
Mapeo de redes genéticas a nivel de célula única
La secuenciación de ARN de célula única es una tecnología innovadora que permite estudiar el transcriptoma de células individuales. Aunque esta técnica ha proporcionado información nueva sobre redes genéticas, no tiene en cuenta posibles limitaciones biofísicas. El proyecto scNet, financiado con fondos europeos, tiene por objeto desarrollar un algoritmo de inferencia que permita predecir el efecto de las perturbaciones de la eliminación en los factores de transcripción. Sus resultados mejorarán el conocimiento de las rutas de regulación en las células y proporcionarán información novedosa sobre cómo interactúan entre sí distintas moléculas. Es más, el trabajo del equipo de scNet ayudará a mapear redes de comunicación genética a nivel de célula única, algo que hasta hace poco era imposible debido a limitaciones metodológicas.
Objetivo
Regulatory processes within living cells have long been the topic of research interest and the key to understanding various diseases. The decades of studies resulted in a large body of knowledge on molecular interactions and regulatory pathways in the cells of model organisms ranging from microorganisms to mammals. Nevertheless, accurately inferring gene network topology at the scale of a whole cell has remained an intractable task until recently, mostly due to the large amount of single-cell data needed for such inference. In the last few years, single-cell RNA sequencing (scRNA-seq) technology enabled measuring transcriptome of high numbers of individual cells, which allowed observing a much grater share of the multidimensional parameter space of large gene networks and gave rise to multiple inference methods. However, none of the existing methods incorporates all relevant knowledge on biophysical constraints. This project aims to incorporate prior knowledge on the system; decomposition of measurement, extrinsic and intrinsic noise; and accurate representation of stochastic gene expression and its regulation into a Bayesian inference framework for identifying topology of a gene network and rate constants of its molecular interactions. The performance of the inference algorithm will be tested by evaluating its ability to predict the effects of transcription factor deletion perturbations. Enhancing gene network inference by accounting for the wealth of known biophysical constraints could provide insights into the gene regulatory processes that would enable advancement in developmental and evolutionary biology, biomedicine and bioengineering.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
64289 Darmstadt
Alemania