Descrizione del progetto
Mappare le reti genetiche a livello monocellulare
Il sequenziamento dell’RNA monocellulare (scRNA-seq) è una nuova tecnologia che consente di indagare sul trascrittoma delle singole cellule. Per quanto l’scRNA-seq abbia fornito degli spunti sulle reti genetiche, non spiega i potenziali vincoli biofisici. Il progetto scNet, finanziato dall’UE, punta a sviluppare un algoritmo di inferenza in grado di prevedere l’impatto in seguito a perturbazioni della delezione dei fattori di trascrizione. I risultati del progetto miglioreranno le nostre conoscenze sui percorsi regolatori nelle cellule e chiariranno il perché molecole diverse interagiscono l’una con l’altra. Ma soprattutto, scNet aiuterà a mappare le reti di comunicazione genetiche a livello monocellulare, un obiettivo fino ad oggi irraggiungibile a causa dei limiti metodologici.
Obiettivo
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.
Campo scientifico
Parole chiave
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Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
64289 Darmstadt
Germania