ArticleSIAM journal on applied mathematics2013
TWO-LAYER MATHEMATICAL MODELING OF GENE EXPRESSION: INCORPORATING DNA-LEVEL INFORMATION AND SYSTEM DYNAMICS.
Article in SIAM journal on applied mathematics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Dynamics of BMP signaling and stable gene expression in the early Drosophila embryo.Biology open · 2024Article
- Order-of-Mutation Effects on Cancer Progression: Models for Myeloproliferative Neoplasm.Bulletin of mathematical biology · 2024Article
- Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm.Mathematical biosciences · 2021Article
- Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.Nucleic acids research · 2021Article
- Expression pattern determines regulatory logic.PloS one · 2021Article
- A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions.Frontiers in microbiology · 2021Review
- Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.BMC systems biology · 2016Article
- Analysis of functional importance of binding sites in the Drosophila gap gene network model.BMC genomics · 2015Article
- Global sensitivity analysis of a dynamic model for gene expression in Drosophila embryos.PeerJ · 2015Article
- Old cell, new trick? Cnidocytes as a model for the evolution of novelty.Integrative and comparative biology · 2014Review
- Quantitative modeling of a gene's expression from its intergenic sequence.PLoS computational biology · 2014Article
- Sequence-based model of gap gene regulatory network.BMC genomics · 2014Article
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4 authors.
Funding
Abstract
High-throughput genome sequencing and transcriptome analysis have provided researchers with a quantitative basis for detailed modeling of gene expression using a wide variety of mathematical models. Two of the most commonly employed approaches used to model eukaryotic gene regulation are systems of differential equations, which describe time-dependent interactions of gene networks, and thermodynamic equilibrium approaches that can explore DNA-level transcriptional regulation. To combine the strengths of these approaches, we have constructed a new two-layer mathematical model that provides a dynamical description of gene regulatory systems, using detailed DNA-based information, as well as spatial and temporal transcription factor concentration data. We also developed a semi-implicit numerical algorithm for solving the model equations and demonstrate here the efficiency of this algorithm through stability and convergence analyses. To test the model, we used it together with the semi-implicit algorithm to simulate a
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