Biology subjects

Flobak, A.

Publications and source records attributed to Flobak, A..

10 recordsLinked to original sources

bioRxiv2026

TRAFIKK: systematic prediction and mechanistic interpretation of anticancer drug synergies

Effective drug combination therapies can improve cancer treatment, yet the mechanistic basis of drug synergy remains poorly understood. Most computational approaches prioritize predictive accuracy over molecular mechanistic interpretability, providing hence limited insights into how synergistic effects emerge across signalling contexts. We developed Trafikk, a molecular-signalling network-based framework that simulates drug perturbations in cell line-specific computational models to mirror functional outcomes in experimental combination screens. Across two independent large-scale datasets, Trafikk identified synergistic combinations with >77% recall. Functional response predictions revealed both conserved and context-dependent mechanisms. While AKT-MEK co-inhibition consistently disrupted coordinated survival and apoptotic signalling in 742 cell lines, PI3K-BCL2 synergy arose through distinct death programs shaped by cell-context-specific network constraints. Trafikk combines predictive performance with mechanistic interpretability, capturing how and why drug synergy emerges across cellular contexts. Source code, installation instructions and usage tutorial are freely available at https://github.com/druglogics/trafikk. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/723755v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@9f09f0org.highwire.dtl.DTLVardef@1456ef5org.highwire.dtl.DTLVardef@177b11forg.highwire.dtl.DTLVardef@10d7435_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Using Constraint-Based Metabolic Modeling to Elucidate Drug-Induced Metabolic Changes in a Cancer Cell Line

Cancer cells frequently reprogram their metabolism to support growth and survival, making metabolic pathways attractive targets for therapy. In this study, we investigated the metabolic effects of three kinase inhibitors and their synergistic combinations in the gastric cancer cell line AGS using genome-scale metabolic models and transcriptomic profiling. We applied the TIDE (Tasks Inferred from Differential Expression) algorithm to infer pathway activity changes and introduced TIDE-essential, a new variant that focuses on task-essential genes, enhancing results robustness. Our results revealed widespread down-regulation of biosynthetic pathways, particularly in amino acid and nucleotide metabolism. Combinatorial treatments induced condition-specific metabolic alterations, including strong synergistic effects in the PI3Ki-MEKi condition affecting ornithine and polyamine biosynthesis. These metabolic shifts provide insight into drug synergy mechanisms and highlight potential therapeutic vulnerabilities. To support reproducibility, we developed an open-source Python package, MTEApy, implementing both TIDE frameworks.

systems biology↗

bioRxiv2025

BooLEVARD: Boolean Logical Evaluation of Activation and Repression in Directed pathways

Boolean models are widely used for studying dynamic processes of biological systems. However, their inherent discrete nature limits their ability to capture continuous aspects of signal transduction, such as signal strength or protein activation levels. Although existing tools provide some path exploration capabilities that can be used to explore signal transduction circuits, the computational workload often requires simplifying assumptions that compromise the accuracy of the analysis. Here, we introduce BooLEVARD, a Python package designed to efficiently quantify the number of paths leading either to node activation or repression in Boolean models, which offers a more detailed and quantitative perspective on how molecular signals propagate through signaling networks. By focusing on the collection of non-redundant paths directly influencing Boolean outcomes, BooLEVARD enhances the precision of signal strength representation. We showcase the application of BooLEVARD in studying cell-fate decisions using a Boolean model of cancer metastasis, demonstrating its ability to identify critical signaling events. Furthermore, BooLEVARD provides a refined understanding of signaling dynamics, which can increase our understanding of disease development and drug responses. BooLEVARD is freely available at https://github.com/farinasm/boolevard.

systems biology↗

bioRxiv2025

High-content morphological profiling by Cell Painting in 3D spheroids.

Cell Painting is a popular assay for morphological profiling of multi-labeled 2D monolayer cell cultures used in a wide range of applications. Culturing cells in 3D has potential for higher physiological relevance, such as when studying effects of perturbations. Robust and scalable 3D models can be challenging to characterize through imaging - particularly because light has difficulty penetrating cell multilayers. We introduce a scalable method where the Cell Painting assay is combined with tissue-clearing and applied to 3D spheroids generated in a ULA microplate format. Multi-channel images are acquired using confocal microscopy, and cells can be segmented inside those spheroids allowing for relevant morphological features to be extracted. Our end-to-end analysis pipeline comprises cell segmentation, morphological feature extraction, and between-spheroids and within-spheroid normalization. We demonstrate the method using spheroids cultured from two colorectal cancer cell lines and successfully detect distinct phenotypic changes upon compound treatments, on both spheroid-level using maximum intensity projections and on single cell-level. We show that drugs group by mechanism of action, with biologically relevant clusters especially evident with single-cell data. Finally, we contrast our method to results from 2D Cell Painting and discover a different pattern in DNA damaging drugs in HCT116 colorectal cancer cells. This work lays the foundation for multi-channel image-based screening in 3D spheroids.

pharmacology and toxicology↗

bioRxiv2024

NeKo: a tool for automatic network construction from prior knowledge

Biological networks provide a structured framework for analyzing the dynamic interplay and interactions between molecular entities, facilitating deeper insights into cellular functions and biological processes. Network construction often requires extensive manual curation based on scientific literature and public databases, a time-consuming and laborious task. To address this challenge, we introduce NeKo, a Python package to automate the construction of biological networks by integrating and prioritizing molecular interactions from various databases. NeKo allows users to provide their molecules of interest (e.g., genes, proteins or phosphosites), select interaction resources and apply flexible strategies to build networks based on prior knowledge. Users can filter interactions by various criteria, such as direct or indirect links and signed or unsigned interactions, to tailor the network to their needs and downstream analysis. We demonstrate some of NeKos capabilities in two use cases: first we construct a network based on transcriptomics from medulloblastoma; in the second, we model drug synergies. NeKo streamlines the network-building process, making it more accessible and efficient for researchers. The tool is available at https://sysbio-curie.github.io/Neko/.

bioinformatics↗

bioRxiv2024

Deciphering molecular mechanisms of synergistic growth reduction in kinase inhibitor combinations

In cancer treatment, the persistent challenge of unresponsiveness of certain patients to drugs or the development of resistance post-treatment remains a significant concern. Drug combinations that synergistically reduce tumor growth emerge as a promising avenue to address this issue. Here, we aimed to characterize the mechanism of action of two synergistic drug combinations that target PI3K together with MEK1 or with TAK1 and used time course measurements of phosphoproteomics and transcriptomics in response to single inhibitors and their combinations. Our analysis untangled those responses driven by single drugs and responses that were unique to the combinations. We observed a high overlap between single-drug responses and their combinations, suggesting that single-drug mechanisms dominate the mechanism of action of the combinations of the kinase inhibitors. Despite a high overlap, both drug combinations exhibited a synergistic modulation of several cell fate regulators found at the convergence points of the targeted pathways, including the key regulator of intrinsic apoptosis BCL2L11. Interestingly, the responses in both combinations were largely limited to the targeted pathways, namely PI3K/AKT and MAPKs, with very limited change of any other additional cell fate decision pathways. In addition, we observed a strong downregulation of nucleotide metabolism and tRNA biosynthesis uniquely in the combinations, which could be attributed to the reduced activity of mTOR and ATF4. Our approach provides insights into the molecular mechanisms affected by the PI3Ki-TAK1i and PI3Ki-MEKi combinations and can serve as a flexible framework for dissecting drug combination responses based on multi-omics measurements.

cancer biology↗

bioRxiv2023

The Minimal Dataset for Cancer of the 1+Million Genomes Initiative

For a real impact on healthcare, precision cancer medicine requires accessibility and interoperability of clinical and genomic data across centres and countries. Due to the heterogeneous digitization in Europe and worldwide, the definition of models for standardised data collection and usability becomes mandatory if countries want to work together on this mission. The European Union 1+Million Genomes (1+MG) initiative, supported by the Horizon 2020 Beyond 1 Million Genomes project, aims at outlining data models, guidance, best practices, and technical infrastructures for transnational access to sequenced genomes, including cancer genomes. Within the framework of the cancer-focused Working Group 9, we developed the 1+MG-Minimal Dataset for Cancer (1+MG-MDC)-a data model encompassing 140 items and organized in eight conceptual domains for the collection of cancer-related clinical information and genomics metadata. The 1+MG-MDC, which results from a multidisciplinary effort, leverages pre-existing models and emphasizes the annotation and traceability of multiple aspects relevant to the complex longitudinal path of the cancer disease and its treatment. We strived to make the 1+MG-MDC easy to adopt, yet comprehensive, addressing the needs of both clinicians and researchers. We will periodically revise and update it to ensure it remains fit for purpose. We propose the 1+MG-MDC as a model to create homogeneous databases, which would, in turn, guide discussions on clinical and genomic features with prognostic or therapeutic value and foster real-world data research.

cancer biology↗

bioRxiv2023

A dynamic Boolean model of molecular and cellular interactions represents psoriasis development and predicts drug candidates.

Psoriasis is a chronic skin disease affecting 2-3% of the global population. Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation and perturbed keratinocyte life cycle. Although the latest generation of drugs have greatly improved psoriasis management, the disease remains incurable, and the substantial variability in treatment response calls for novel approaches to comprehend the intricate mechanisms underlying disease development and to discover potential drug targets. In this study, we present a multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network metrics, we identify potential pairwise interventions as alternative treatment options. Specifically, The model predictions suggest that targeting neutrophil activation in conjunction with either PGE2 production or STAT3 signaling shows promise comparable to IL-17 inhibition, which is currently the most used treatment option for moderate and severe cases of psoriasis. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling cascades in psoriasis, and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification. Author summaryIn our study, we aimed to uncover the complex mechanisms underlying psoriasis and identify potential treatment options. By utilizing a computational model, we simulated the dynamic interactions between different cell types involved in psoriasis, such as immune cells and keratinocytes. Our model predicts that targeting neutrophil activation, combined with either PGE2 production or STAT3 signaling, may yield comparable effectiveness to the current standard treatment for moderate or severe psoriasis, namely IL-17 inhibition. Our study underscores the importance of computational modeling in unraveling the complexities of disease systems and provides a foundation for identifying new candidate treatment options in psoriasis that should be tested in the lab.

systems biology↗

bioRxiv2021

Logical modeling: Combining manual curation and automated parameterization to predict drug synergies

Treatment with drug combinations carries great promise for personalized therapy. We have previously shown that drug synergies targeting cancer can manually be identified based on a logical framework. We now demonstrate how automated adjustments of model topology and logic equations can greatly reduce the workload traditionally associated with logical model optimization. Our methodology allows the exploration of larger model ensembles that all obey a set of observations. We benchmark synergy predictions against a dataset of 153 targeted drug combinations. We show that well-performing manual models faithfully represent measured biomarker data and that their performance can be outmatched by automated parameterization using a genetic algorithm. The predictive performance of a curated model is strongly affected by simulated curation errors, while data-guided deletion of a small subset of edges can improve prediction quality. With correct topology we find some tolerance to simulated errors in the biomarker calibration data. With our framework we predict the synergy of joint inhibition of PI3K and TAK1, and further substantiate this prediction with observation in cancer cell cultures and in xenograft experiments.

systems biology↗

bioRxiv2019

A reference model for the combination of an arbitrary number of drugs: A generalization of the Bliss independence model

It is commonplace to determine the effectiveness of the combination of drugs by comparing the observed effects to a reference model that describes the combined effect under the assumption that the drugs do not interact. Depending on what is to be understood by non-interacting behavior, several reference models have been developed in the literature. One of them is the celebrated Bliss independence model, which assimilates non-interaction with statistical independence. Intuitively, this requires the dose-response curves to have zero as minimal effect and one as maximal effect, a restriction that was indeed adopted by Bliss. However, we show how non-interaction can be interpreted in terms of statistical independence, while nevertheless allowing arbitrary values for the minimal and the maximal effect. Furthermore, our reference model allows the maximal effects of the dose-response curves to be different. In a first step, we construct a basic reference model for the case of two drugs and where the maximal effects of the two individual dose-response curves are assumed to be equal. By relying on the notion of non-interaction in terms of statistical independence, and by introducing two consistency principles, we show how a unique reference model can be derived. In a second step, a more general reference model, allowing the maximal effects to be different while still restricting to two drugs, is then easily constructed from the basic reference model. Finally, an induction step is applied to generalize the reference model to the case of an arbitrary number of drugs, allowing each dose-response curve to have a possibly different maximal effect. Although the minimal effect of the dose-response curves are restricted to be equal, which we show to be a necessary consequence of consistency rules, its value is arbitrary.\n\nAuthor summaryThe Bliss independence model is a very popular reference model for drug combinations, meaning that it predicts the combined effect of doses of given drugs under the assumption of non-interaction between these drugs. However, because Bliss described non-interaction as statistical independence, he thought that he had to assume that the minimal effect of all dose-response curves are zero, while the maximal effect of all dose-response curves are one. While it is acceptable that all dose-response curves have minimal effect zero, because this amounts to having a common reference state (i.e. the response when no drug at all is given), it is a severe restriction to force all dose-response curves to have maximal effect one. On the other hand, the Bliss independence model has the advantage that it relies on sound statistical theory, and the assimilation of non-interaction with statistical independence is rather intuitive. We have extended the Bliss independence model to allow the involved dose-response curves to have different maximal effects. This has been done in a rigorous way, where the statistical underlying theory that was used by Bliss remains essentially intact.

cancer biology↗