ABSTRACT. Inferring culture media that enable specific metabolic functions is a challenging problem due to the vast combinatorial search space induced by all the possible subsets of compounds and reactions within a metabolic network. Existing scalable approaches based on flux optimization or combinatorial enumeration do not integrate prior biological knowledge to tailor the media to the cell physiological context. We introduce a method that solves culture media inference constrained by biological observation and knowledge as a combinatorial optimization problem, implemented using Answer Set Programming (ASP). This method combines four heuristics that progressively restrict the search space toward biologically supported solutions: (i) identify the target synthesis subnetwork achieving target compounds production, (ii) compute the parsimonious network merging the minimal reaction sets contextualized with the provided constraints, (iii) enumerate minimal media from this reduced search space, and (iv) detect and filter out the self activating internal compounds from the solution space, required for algorithmic reasons but not informative as environmental inputs. We evaluate this approach on different genome-scale metabolic networks and test varying biological constraints and heuristic combinations to assess the method's scalability. This method provides a knowledge-driven and scalable enumeration of minimal culture media, combining logical reasoning, minimality optimizations, as well as knowledge and topology-based constraints to address the combinatorial complexity of the reverse ecology problem.

ABSTRACT. The Chemical Reaction Networks (CRN) interpreted through the differential semantics, even when restricted to elementary reactions with mass action law kinetics, form a Turing-complete language. This means that any computable real function can thus be programmed, and in fact compiled, in an abstract CRN that will compute it with an arbitrarily high precision. In this computational framework, the information carriers are the molecular concentrations, the required precision is given as input, and the output concentration is guaranteed to satisfy the required precision. On the other hand, one can be interested in estimating the derivative of an unknown input signal or in reading the concentration value of an input molecular species. By nature, such problems can only be approximated with a finite precision. Hence, the computation framework proposed previously cannot be applied and we need to design and analyze custom CRNs to perform these tasks. In this paper, we present an analog-dyadic converter CRN which takes as input one molecular concentration (in $[0, 1]$ but not necessarily computable), and produces as output a sequence of ``on'' and ``off'' spikes corresponding to some extent to the sequence of bits in the dyadic representation of the input concentration. We provide a detailed analysis of the source of errors and their behavior when varying the reactions rate constants. We conclude by sketching a possible design for a reader module that takes as input an arbitrary concentration and a desired precision and outputs a dyadic encoding approximating the value of the concentration with the desired

ABSTRACT. Transcriptional networks represent one of the most extensively studied types of systems in synthetic biology. Although the completeness of transcriptional networks for digital logic is well-established, analog computation plays a crucial role in biological systems and offers significant potential for synthetic biology applications. While transcriptional circuits typically rely on cooperativity and highly nonlinear behavior of transcription factors to regulate protein production, they are often modeled with simple linear degradation terms. In contrast, general analog dynamics require both positive and negative nonlinear terms, seemingly necessitating control over not just transcriptional (i.e., production) regulation but also the degradation rates of transcription factors. Surprisingly, we prove that controlling transcription factor production (i.e., transcription rate) without explicitly controlling degradation is mathematically complete for analog computation, achieving equivalent capabilities to systems where both production and degradation are programmable. We demonstrate our approach on several examples including oscillatory and chaotic dynamics, analog sorting, memory, PID controller, and analog extremum seeking. Our result provides a systematic methodology for engineering novel analog dynamics using synthetic transcriptional networks without the added complexity of degradation control and informs our understanding of the capabilities of natural transcriptional circuits. We provide a compiler, in the form of a Python package that can take any system of polynomial ODEs and convert it to an equivalent transcriptional network implementing the system exactly, under appropriate conditions.

ABSTRACT. Aging and Alzheimer’s disease are complex multifactorial processes driven by interacting genetic and metabolic mechanisms, for which single-gene approaches often fail to capture system-level behaviour. Systems biology addresses this challenge by integrating omics data with genome-scale metabolic models (GSMMs) to identify candidate interventions. The Metabolic Transformation Algorithm (MTA) and its robust variant rMTA use constraint-based modelling to predict perturbations that shift a system from a disease-associated toward a healthier metabolic state. To move beyond single-gene prioritization, we applied EA-rMTA, an evolutionary extension of rMTA that searches the combinatorial knockout space by optimizing the robust Transformation Score. We evaluated the framework in two case studies: lifespan-extending unc-62 knockdown in Caenorhabditis elegans and early-onset Alzheimer’s disease (EOAD) in human cortex. In both settings, rMTA found biologically coherent targets and pathway-level signatures consistent with known hallmarks of aging and neurodegeneration. EA-rMTA further identified compact and interpretable multi-gene intervention strategies that outperformed single-gene deletions in shifting the metabolic state toward the desired phenotype. Together, these results show that evolutionary search extends metabolic transformation analysis beyond single-gene perturbations and enables the discovery of experimentally tractable combinatorial intervention hypotheses. More broadly, the framework provides a computational strategy for generating systems-level therapeutic hypotheses in aging, neurodegeneration, and other multifactorial disorders.

ABSTRACT. Microbial communities play a central role in many bioprocesses with key applications in food fermentation, waste treatment, human and animal well-being, plant protection or metabolite transformation in industrial bioprocesses. However, the metabolic microbial interactions driving the community dynamics remain difficult to characterize because of their complexity and their temporal variability. Recent advances in sequencing and analytical technologies now provide time-resolved multi-omics data at the community scale providing key insights into the mechanisms shaping the community dynamics. However, integrating these heterogeneous data in an interpretable way to decipher species-specific metabolic activity and microbial interactions remains a major challenge in the study of microbial communities. We introduce the community metabolic flux inference (comFI) method, a mathematical framework for inferring the metabolic fluxes of individual microorganisms from community-level longitudinal data. The method formulates flux estimation as a biology-informed constrained inference problem that combines observed microbial abundances and extracellular metabolite exchange data, with metabolic constraints encoded in a metabolic model and transcriptomic-based lasso regularization terms. We evaluated comFI on synthetic datasets generated from dynamic models of microbial communities involving three Escherichia coli mutant strains. The comFI method showed a very good reconstruction accuracy for exchange fluxes, intracellular metabolic fluxes distribution, metabolic pathway activation patterns and strain contribution. We also applied the method to experimental cheese fermentation data involving three bacteria (Lactococcus lactis, Lactobacillus plantarum, and Propionibacterium freudenreichii), combining abundance measurements, targeted metabolomics and metatranscriptomics data. The comFI framework enabled to recover previously identified interaction patterns, and to reconstruct latent intracellular flux states for individual microorganisms alongside with their respective metabolic contributions within the community, consistently with the omics data and known physiology. All together, we demonstrate that comFI provides a practical framework for recovering the metabolic activity of individual microorganisms from community-scale multi-omics time-resolved data.

ABSTRACT. Gene Regulatory Networks (GRNs) constitute a useful abstraction of complex molecular mechanisms responsible for cell behaviors, cell differentiation, and various diseases. Advances in high-throughput transcriptomics, such as single cell RNA sequencing data, have enabled unprecedented access to large-scale gene expression data. However, the manual reconstruction of GRNs from such data and literature is a hard task, and the automatic reconstruction by GRN inference algorithms remains a major challenge to analyze and validate. Previous work revealed that the performances of GRN inference algorithms vary significantly according to network topology, degree distribution and motif preponderance. The analysis of sensitivity to those properties requires the generation of synthetic GRNs of various forms, and expression data obtained by simulation. Since the seminal works of Barabási and Albert on the scale-free power-law distribution of connectivity degrees in GRNs, and of Alon and Milo on the particular distributions and significance of small network motifs, various random graph models have been developed to try to capture those particular features. In this paper, we show that a relaxed version of the directed configuration model (RDCM) does allow us to generate random GRNs fitting the graph properties and motif distributions of several large GRNs of the literature: Human-TRRUST, h-ESC,m-ESC,m-DC,and to a lessere xtent Yeast and E. coli, thanks to the variance of the results, and despite the discrepancies previously observed in terms of the mean in that random graph model. This is shown with GRNgen, a Python package which takes as input the number of nodes with for each node its in-degree and out-degree, the average path length, diameter, number of arcs and 7 important motif counts, and generates as output a set of random GRNs ranked by their fit to the input properties. We present our results for the generation of 1,000 samples for each of our reference GRNs, and provide elements of comparison with other generators.

ABSTRACT. Discovering reliable cause-and-effect relationships in real-world med- ical data is an open challenge. Classical Causal Discovery (CD) algorithms used to solve this task rely on strict assumptions that are rarely met in complex real- world scenarios with limited expert knowledge - the functional form of the causal relationships, the data distribution, the causal sufficiency. Thus, the reliability of CD algorithms can significantly drop, compromising the interpretability of the results and the trustworthiness of downstream decision-making. To overcome these limitations, we introduce the concept of consensus causal model to com- bine various CD algorithms and enhance their accuracy. Our consensus model can be efficiently constructed from a set of heterogeneous causal graph objects through a homogenisation step, ensuring semantic compatibility with the original edge definitions and enabling meaningful information exchange. To showcase the proposed method, we analyze a lung cancer dataset combining patient-level in- formation such as smoking habits and age, and we study their effect on the onset and development of the disease, the tumor stage, and cellular pathway mutations. By applying multiple classical CD algorithms, we observe significant structural inconsistencies and heterogeneity across individual graphs. We demonstrate that the consensus causal model, unlike the individual models, effectively aggregates the strengths of each algorithm while mitigating their uncertainties. The result- ing model reveals biologically validated causal relationships between risk factors, mutations, and pathways that isolated algorithms fail to capture, thereby under- scoring the value of consensus causal modelling as a robust alternative to single- model selection for causal discovery.

ABSTRACT. Predicting gene expression dynamics is challenging due to the complex regulatory interactions within high-dimensional datasets. We evaluate predictive models that integrate temporal patterns with gene–gene networks, comparing a state-of-the-art approach based on Protein-Protein Interaction (PPI) networks from STRING with models utilizing data-driven network inference. Our results show that inferred networks can enhance accuracy over static biological priors. However, simpler models treating genes independently often achieve comparable performance. This suggests that for the considered datasets, the added complexity of explicit gene–gene interactions does not always translate into superior predictive power, opening to further investigations on the most effective ways to represent and leverage biological connectivity in forecasting tasks.

ABSTRACT. Gliomas are the most common type of brain tumor in adults and are associated with poor prognosis and high mortality. Despite technological advances, their classification remains challenging in both clinical practice and research, highlighting the need for improved molecular subtyping to enhance diagnosis and treatment. In this study, we systematically evaluate the robustness of current glioma classification using a reproducible pipeline for unsupervised patient stratification. We apply multiple multi-view clustering methods, namely, CIMLR, intNMF, and moCluster, to cluster glioma patients based on transcriptomics and methylomics data. All methods consistently distinguished glioblastoma (GBM) from lower-grade gliomas, with survival analysis confirming poorer outcomes for GBM clusters, in line with clinical expectations. The best partition was obtained with CIMLR, closely aligning with the 2021 WHO classification of central nervous system tumors. In contrast, intNMF identified three clusters with distinct survival distributions, suggesting additional biological heterogeneity that warrants further investigation. To enhance robustness and better capture tumor complexity, we implemented consensus clustering to integrate results across methods. This integrative approach yielded well-defined groups with improved concordance to established glioma subtypes compared to individual methods. Notably, the consensus solution identified more clusters than the traditional classification, revealing deeper molecular characterization and providing a framework to refine glioma classification. Furthermore, we identified key molecular features supported by the literature, including both established and emerging biomarkers. Overall, our findings underscore the value of multi-omics integration and consensus clustering in refining glioma classification, supporting the discovery of novel biomarkers to improve diagnostics, prognostics, and patient outcomes.