Predicting weighted unobserved nodes in a regulatory network using answer set programming.

BACKGROUND: The impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our global understanding of biological mechanisms triggered by a perturbation. This study focuses on improving the modelling of the regulatory network to facilitate a possible integration with the metabolic network. Previously proposed methods that study this problem fail to deal with a real-size regulatory network, computing predictions sensitive to perturbation and quantifying the predicted species behaviour more finely. RESULTS: To address previously mentioned limitations, we develop a new method based on Answer Set Programming, MajS. It takes a regulatory network and a discrete partial set of observations as input. MajS tests the consistency between the input data, proposes minimal repairs on the network to establish consistency, and finally computes weighted and signed predictions over the network species. We tested MajS by comparing the HIF-1 signalling pathway with two gene-expression datasets. Our results show that MajS can predict 100% of unobserved species. When comparing MajS with two similar (discrete and quantitative) tools, we observed that compared with the discrete tool, MajS proposes a better coverage of the unobserved species, is more sensitive to system perturbations, and proposes predictions closer to real data. Compared to the quantitative tool, MajS provides more refined discrete predictions that agree with the dynamic proposed by the quantitative tool. CONCLUSIONS: MajS is a new method to test the consistency between a regulatory network and a dataset that provides computational predictions on unobserved network species. It provides fine-grained discrete predictions by outputting the weight of the predicted sign as a piece of additional information. MajS' output, thanks to its weight, could easily be integrated with metabolic network modelling.

Boolean Network Models of Human Preimplantation Development.

Single-cell transcriptomic studies of differentiating systems allow meaningful understanding, especially in human embryonic development and cell fate determination. We present an innovative method aimed at modeling these intricate processes by leveraging scRNAseq data from various human developmental stages. Our implemented method identifies pseudo-perturbations, since actual perturbations are unavailable due to ethical and technical constraints. By integrating these pseudo-perturbations with prior knowledge of gene interactions, our framework generates stage-specific Boolean networks (BNs). We apply our method to medium and late trophectoderm developmental stages and identify 20 pseudo-perturbations required to infer BNs. The resulting BN families delineate distinct regulatory mechanisms, enabling the differentiation between these developmental stages. We show that our program outperforms existing pseudo-perturbation identification tool. Our framework contributes to comprehending human developmental processes and holds potential applicability to diverse developmental stages and other research scenarios.

The SSV-Seq 2.0 PCR-Free Method Improves the Sequencing of Adeno-Associated Viral Vector Genomes Containing GC-Rich Regions and Homopolymers.

Adeno-associated viral vectors (AAV) are efficient engineered tools for delivering genetic material into host cells. The commercialization of AAV-based drugs must be accompanied by the development of appropriate quality control (QC) assays. Given the potential risk of co-transfer of oncogenic or immunogenic sequences with therapeutic vectors, accurate methods to assess the level of residual DNA in AAV vector stocks are particularly important. An assay based on high-throughput sequencing (HTS) to identify and quantify DNA species in recombinant AAV batches is developed. Here, it is shown that PCR amplification of regions that have a local GC content >90% and include successive mononucleotide stretches, such as the CAG promoter, can introduce bias during DNA library preparation, leading to drops in sequencing coverage. To circumvent this problem, SSV-Seq 2.0, a PCR-free protocol for sequencing AAV vector genomes containing such sequences, is developed. The PCR-free protocol improves the evenness of the rAAV genome coverage and consequently leads to a more accurate relative quantification of residual DNA. HTS-based assays provide a more comprehensive assessment of DNA impurities and AAV vector genome integrity than conventional QC tests based on real-time PCR and are useful methods to improve the safety and efficacy of these viral vectors.