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Systems biology studies the interactions and behavior of the components of biological entities, such as molecules, cells, organs, and organisms. This discipline emphasizes the central role of complex interactions within biological systems, as opposed to studying their components in isolation.
The regulation of gene expression has important implications for both fundamental biology and numerous biomedical applications. The study of gene expression in systems biology relies on mathematical and computational modeling of experimental data. These have seen significant progress over the last decade with the advent of single-cell RNA sequencing (scRNA-seq), which
allows gene expression to be measured with unprecedented resolution. A first step in modeling and understanding gene expression is to infer the interactions through which genes might influence each other. Currently, statistical and deep learning methods are used, in parallel with bioinformatic analyses, to deduce plausible interactions from scRNA-seq and other data sources. The set of inferred interactions then constitutes a gene regulatory network (GRN), a static model of the influences between genes. GRNs provide valuable information, but their description does not explain the regulatory mechanisms behind the observed biological phenomena. Dynamic models aim to fill this gap with a mechanistic view capable of explaining and reproducing the variations observed in the data. Boolean networks (BNs) are a special class of dynamic systems commonly used to model gene expression regulation. In these models, the activity of biological entities is represented as either active or inactive. This binary representation allows us to reason about causal relationships between entities without having to estimate kinetic parameters or regulatory thresholds. Despite recent advances in this field, many questions regarding the modeling of scRNA data using BNs remain open. In my thesis, I am working on two main areas concerning BNs and scRNA-seq data.
The first axis consists of linking scRNA-seq data, which is quantitative in nature, to the qualitative states of BNs. To this end, my first contribution is the new scBoolSeq method, which provides a qualitative interpretation of experimental scRNA-seq data and also allows the generation of synthetic scRNA-seq data that takes into account the Boolean states of gene activation. scBoolSeq has already been used to compare inference methods using the aforementioned synthetic scRNA-seq data reflecting Boolean dynamics. The second approach consists of applying the model training and evaluation methodology used in statistical learning to BN inference. This is often done manually and only takes isolated experiments into account. My proposal is to exploit several experimental conditions as follows: By inferring Boolean networks on “training” experimental conditions and measuring their predictive power on validation data, it is possible to evaluate and subsequently optimize their predictive power. Thus, my research represents a new direction that combines formal methods with statistical learning to improve the modeling of scRNA-seq data using Boolean networks.

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