Data Integration in Logic-based Models of Biological Mechanisms

Hall, Benjamin D.; Niarakis, Anna

doi:10.20944/preprints202105.0219.v1

Cited by 4 publications

(4 citation statements)

References 45 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To do so, we used the possibility of adding Boolean rules to the network with the tool CaSQ [29]. Boolean models have been long used to describe biological mechanisms in health and disease [38], and they are an optimal approach for modelling signalling and gene regulation, when kinetic parameters are scarce. Boolean models are qualitative by nature, based on the assignment of binary values to the variables and the use of the logical operators AND, OR and NOT to describe the regulation of all molecules in the system [21].…”

Section: Subnetwork Logic-based Dynamical Analysismentioning

confidence: 99%

Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Miagoux

Mezquita

Singh

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

MotivationRheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints. RA is considered a complex disease as it involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this approach, we integrate three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up and top-down methods to build an integrative, disease-specific network. The goal behind this endeavour is to see if we can unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and derive patient-specific models to gain more insights into the disease heterogeneity.ResultsIn this work, we combine biological data relevant to Rheumatoid Arthritis, in the form of a global, integrative network. We first make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using prior knowledge encoded in a state-of-the-art, RA-specific molecular map. Lastly, a list of RA specific variants highlights key genes associated with known disease mutations.AvailabilityDatasets used for the analysis are publicly available. All scripts used to generate results and the Shiny app will be freely accessible after peer-reviewed publication.

show abstract

Section: Subnetwork Logic-based Dynamical Analysismentioning

confidence: 99%

Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Miagoux

Mezquita

Singh

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some databases will provide information on functional interactions, i.e., evidence that 2 genes/proteins, in this case a TF-target pair, can be related based on any type of link between them, evidenced by gene expression correlation, coevolution of the genes, comention in scientific abstracts, etc. These functional interactions can be found in databases such as String [113], Reactome (the functional interaction network [114], TRRUST [115], or RegNetwork [116], the latest selecting information from around 25 databases. Moreover, there are efforts to map more specific TFtarget interactions based on experimental evidence, either collecting results of ChIP-seq experiments that show in which target gene promoters the binding peak of the TF can be found or also combining these experimental results with a crosscheck of the presence of the TF binding motif in the peak regions.…”

Section: Grn Validationmentioning

confidence: 99%

“…Particularly in Boolean models, similar to using time-series of expression data for GRN inference, the temporal information in the changes of the genes' expression is also used to infer the Boolean rules that govern these changes [115,[156][157][158][159][160][161], which can be then studied using several available tools [162]. More recently, patient-specific Boolean models have been developed to suggest targeted therapy to patients based on their 'omics profile [163].…”

Section: From Static To Dynamic Networkmentioning

confidence: 99%

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Marku

Pancaldi

2023

PLoS Comput Biol

View full text Add to dashboard Cite

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

show abstract

“…Computational modeling is essential to gain insight on the emergent behavior of biological entities when complex and interconnected pathways are involved. Qualitative models based on logical relationships between components provide an appropriate description for systems whose mechanistic processes are unknown or lack quantitative data [15,16]. They allow a parameter-free study of the underlying dynamical properties of large biological pathways.…”

Section: Introductionmentioning

confidence: 99%

Hybrid computational modeling highlights reverse Warburg effect in breast cancer-associated fibroblasts

Aghakhani,

Silva-Saffar,

Soliman

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Cancer-associated fibroblasts (CAFs) are key players of the tumor microenvironment (TME) involved in cancer initiation, progression, and resistance to therapy. These cells exhibit aggressive phenotypes affecting, among others, extracellular matrix remodeling, angiogenesis, immune system modulation, tumor growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations, notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells, leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple biological layers are essential to capture the emergent properties of various biological entities when complex and intertwined pathways are involved. This work presents the first hybrid large-scale computational model for breast CAFs covering major cellular signaling, gene regulation, and metabolic processes. It was generated by combining an asynchronous cell- and disease-specific regulatory Boolean model with a generic core metabolic network leveraging both data-driven and manual curation approaches. This model reproduces the experimentally observed reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, considering our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis.All analyses are compiled and thoroughly annotated in Jupyter notebooks and R scripts available on a GitLab repository (https://gitlab.com/genhotel/breast-cafs-reverse-warburg-effect) and a Zenodo permanent archive [1].

show abstract

Data Integration in Logic-based Models of Biological Mechanisms

Cited by 4 publications

References 45 publications

Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Inference of an integrative, executable network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

From time-series transcriptomics to gene regulatory networks: A review on inference methods

Hybrid computational modeling highlights reverse Warburg effect in breast cancer-associated fibroblasts

Contact Info

Product

Resources

About