Enhancing gene network inference from single-cell transcriptomics data through biophysical constraints

Project Information

scNet

Grant agreement ID: 101033300

DOI

10.3030/101033300

Project terminated on 31 March 2022

EC signature date 5 March 2021

Start date 1 April 2021

End date 31 March 2023

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 174 806,40

EU contribution

€ 174 806,40

Coordinated by

TECHNISCHE UNIVERSITAT DARMSTADT
Germany

Project description

Mapping gene networks at the single-cell level

Single-cell RNA sequencing (scRNA-seq) is a novel technology that enables the investigation of the transcriptome of individual cells. Although scRNA-seq has provided insight into gene networks, it does not account for potential biophysical constraints. The EU-funded scNet project aims to develop an inference algorithm that can predict the impact following transcription factor deletion perturbations. The project's results will enhance our knowledge of regulatory pathways in cells and shed light on how different molecules interact with each other. Importantly, scNet will help map gene communication networks at the single-cell level, which has proved impossible until recently due to methodological limitations.

Objective

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.

Fields of science (EuroSciVoc)

• natural sciences › mathematics › applied mathematics › statistics and probability › bayesian statistics
• natural sciences › mathematics › pure mathematics › topology
• natural sciences › biological sciences › evolutionary biology

Keywords

• single-cell RNA sequencing
• integrative analysis of heterogeneous data
• noise decomposition
• network inference
• network perturbation analysis

Programme(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions MAIN PROGRAMME
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Topic(s)

• MSCA-IF-2020 - Individual Fellowships
  

Funding Scheme

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

Call for proposal

H2020-MSCA-IF-2020

Coordinator

TECHNISCHE UNIVERSITAT DARMSTADT

• Net EU contribution: € 174 806,40
• Address: KAROLINENPLATZ 5 ; 64289 DARMSTADT ; Germany
• Region: Hessen › Darmstadt › Darmstadt, Kreisfreie Stadt
• Activity type: Higher or Secondary Education Establishments
• Total cost: € 174 806,40