We deliver comprehensive systems biology modeling powered by advanced AI algorithms. Our platform reconstructs biological networks, performs dynamic simulation, and identifies key regulatory mechanisms across metabolic, gene regulatory, and protein interaction networks.
Biological systems are inherently complex networks of interacting components. Our AI-driven platform transforms this complexity into actionable insights by enabling rapid, accurate reconstruction and analysis of biological networks at unprecedented scales.
We combine proven systems biology methods with cutting-edge AI to deliver accurate, comprehensive network modeling.
AI-powered reconstruction of biological networks from multi-omics data using graph neural networks and Bayesian inference.
Neural network-constrained models enable genome-scale dynamic simulation of cellular signaling and metabolic networks.
Comprehensive topological and functional analysis to identify key regulators, modules, and disease-associated pathways.
From network reconstruction to dynamic simulation, our platform covers every aspect of systems biology modeling.
Genome-scale metabolic model reconstruction and analysis for any organism.
TF-gene interaction inference and regulatory mechanism analysis.
PPI network reconstruction and link prediction for functional annotation.
Pathway modeling and perturbation response simulation.
Traditional network analysis relies on manual curation and static models. Our AI platform enables dynamic, predictive systems biology at unprecedented scale.
AI accelerates network reconstruction from omics data, reducing months of manual curation to hours of automated analysis.
Graph neural networks preserve biological network topology, capturing interaction patterns traditional methods miss.
Move beyond static maps with neural network-constrained models that predict system behavior over time.
Identify key network hubs, modules, and pathways with explainable AI that highlights evidence for each prediction.
See how our systems biology modeling has advanced research across multiple domains.
Constructed genome-scale metabolic models to identify optimal gene knockout targets for enhanced production of bioproducts.
Metabolic EngineeringIndustrial BiotechApplied network analysis to identify disease-associated modules and prioritize candidate genes for follow-up studies.
Network MedicineDrug DiscoveryModeled gene regulatory circuits to optimize synthetic biology constructs for therapeutic protein production.
Synthetic BiologyTherapeuticsOur systematic approach ensures robust and reproducible systems biology modeling.
Aggregate multi-omics data and public database resources for network reconstruction.
Apply AI algorithms for network reconstruction with confidence scores.
Perform topological analysis, module detection, and experimental validation.
Build and simulate dynamic models for perturbation analysis.
Our methods are based on peer-reviewed research from leading computational biology and systems biology journals.
Song, Q., Ruffalo, M., & Bar-Joseph, Z. (2023). Using single cell atlas data to reconstruct regulatory networks. Nucleic Acids Research, 51(7), e38.
Nilsson, A., Peters, J.M., Meimetis, N., et al. (2022). Artificial neural networks enable genome-scale simulations of intracellular signaling. Nature Communications, 13(1), 3069.
Shu, H., Zhou, J., Lian, Q., et al. (2021). Modeling gene regulatory networks using neural network architectures. Nature Computational Science, 1(7), 491-501.
Balogh, O.M., Benczik, B., Horváth, A., et al. (2022). Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network model. BMC Bioinformatics, 23(1), 78.
Long, Y., Wu, M., Liu, Y., Fang, Y., et al. (2022). Pre-training graph neural networks for link prediction in biomedical networks. Bioinformatics, 38(9), 2482-2490.
We model various biological networks including metabolic networks, gene regulatory networks, protein-protein interaction networks, signal transduction pathways, and integrated multi-network systems. Our platform handles both small-scale focused models and genome-scale reconstructions.
AI accelerates network reconstruction from omics data, predicts missing interactions, identifies key network hubs and modules, optimizes model parameters, and enables dynamic simulation at scales impossible through manual curation.
We can work with various omics data including transcriptomics, proteomics, metabolomics, and interactomics datasets. Genome sequences, pathway databases, and literature can also be integrated. Our team will advise on optimal data requirements based on your specific modeling objectives.
We have pre-built models for all major model organisms including human, mouse, rat, zebrafish, fruit fly, C. elegans, yeast, E. coli, and Arabidopsis. We can also create custom references for any organism.
Yes. Beyond static network maps, our AI enables dynamic simulation of biological systems. We model time-dependent behaviors, cellular responses, and system-level effects of genetic or pharmacological perturbations using neural network-constrained models.
Let our AI-powered platform help you reconstruct, analyze, and simulate biological networks for your research.
Biological systems are inherently complex networks of interacting components. Our AI-driven platform transforms this complexity into actionable insights by enabling rapid, accurate reconstruction and analysis of biological networks at unprecedented scales.
We combine proven systems biology methods with cutting-edge AI to deliver accurate, comprehensive network modeling.
AI-powered reconstruction of biological networks from multi-omics data using graph neural networks and Bayesian inference.
Neural network-constrained models enable genome-scale dynamic simulation of cellular signaling and metabolic networks.
Comprehensive topological and functional analysis to identify key regulators, modules, and disease-associated pathways.
From network reconstruction to dynamic simulation, our platform covers every aspect of systems biology modeling.
Genome-scale metabolic model reconstruction and analysis for any organism.
TF-gene interaction inference and regulatory mechanism analysis.
PPI network reconstruction and link prediction for functional annotation.
Pathway modeling and perturbation response simulation.
Traditional network analysis relies on manual curation and static models. Our AI platform enables dynamic, predictive systems biology at unprecedented scale.
AI accelerates network reconstruction from omics data, reducing months of manual curation to hours of automated analysis.
Graph neural networks preserve biological network topology, capturing interaction patterns traditional methods miss.
Move beyond static maps with neural network-constrained models that predict system behavior over time.
Identify key network hubs, modules, and pathways with explainable AI that highlights evidence for each prediction.
See how our systems biology modeling has advanced research across multiple domains.
Constructed genome-scale metabolic models to identify optimal gene knockout targets for enhanced production of bioproducts.
Metabolic EngineeringIndustrial BiotechApplied network analysis to identify disease-associated modules and prioritize candidate genes for follow-up studies.
Network MedicineDrug DiscoveryModeled gene regulatory circuits to optimize synthetic biology constructs for therapeutic protein production.
Synthetic BiologyTherapeuticsOur systematic approach ensures robust and reproducible systems biology modeling.
Aggregate multi-omics data and public database resources for network reconstruction.
Apply AI algorithms for network reconstruction with confidence scores.
Perform topological analysis, module detection, and experimental validation.
Build and simulate dynamic models for perturbation analysis.
Our methods are based on peer-reviewed research from leading computational biology and systems biology journals.
Song, Q., Ruffalo, M., & Bar-Joseph, Z. (2023). Using single cell atlas data to reconstruct regulatory networks. Nucleic Acids Research, 51(7), e38.
Nilsson, A., Peters, J.M., Meimetis, N., et al. (2022). Artificial neural networks enable genome-scale simulations of intracellular signaling. Nature Communications, 13(1), 3069.
Shu, H., Zhou, J., Lian, Q., et al. (2021). Modeling gene regulatory networks using neural network architectures. Nature Computational Science, 1(7), 491-501.
Balogh, O.M., Benczik, B., Horváth, A., et al. (2022). Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network model. BMC Bioinformatics, 23(1), 78.
Long, Y., Wu, M., Liu, Y., Fang, Y., et al. (2022). Pre-training graph neural networks for link prediction in biomedical networks. Bioinformatics, 38(9), 2482-2490.
We model various biological networks including metabolic networks, gene regulatory networks, protein-protein interaction networks, signal transduction pathways, and integrated multi-network systems. Our platform handles both small-scale focused models and genome-scale reconstructions.
AI accelerates network reconstruction from omics data, predicts missing interactions, identifies key network hubs and modules, optimizes model parameters, and enables dynamic simulation at scales impossible through manual curation.
We can work with various omics data including transcriptomics, proteomics, metabolomics, and interactomics datasets. Genome sequences, pathway databases, and literature can also be integrated. Our team will advise on optimal data requirements based on your specific modeling objectives.
We have pre-built models for all major model organisms including human, mouse, rat, zebrafish, fruit fly, C. elegans, yeast, E. coli, and Arabidopsis. We can also create custom references for any organism.
Yes. Beyond static network maps, our AI enables dynamic simulation of biological systems. We model time-dependent behaviors, cellular responses, and system-level effects of genetic or pharmacological perturbations using neural network-constrained models.
Let our AI-powered platform help you reconstruct, analyze, and simulate biological networks for your research.
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