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The Notch signaling pathway is a major intracellular signaling pathway that is highly conserved over a broad spectrum of metazoan species. It stands unique from other intracellular signaling mechanisms in animals because notch protein itself acts as the receptor as well as the primary signaling molecule.
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The Hedgehog gene (Hh) was first discovered due to its control of the growth of disorganized, hair-like bristles phenotype in Drosophila, much like hedgehog spines. Hh plays a crucial role in the development of organs and the maintenance of homeostasis in both invertebrates and vertebrates. However, while Drosophila has only one Hh protein, mammals have multiple functional Hedgehog proteins - Sonic (Shh), Desert (Dhh), and Indian Hedgehog (Ihh). All of these homologous proteins have adapted to...
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Wnt is a zygotic effect gene that is expressed during very early embryonic development. It regulates various processes in animals starting from early development through the adult stage, such as organogenesis in the embryo and maintenance of neuronal and blood stem cells. Wnt proteins can induce a wide variety of intracellular pathways depending upon the specific abilities of different Wnt ligands to form a complex with shared and cognate receptors in the presence of different co-receptors. The...
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The gene encoding the main signaling molecules of the Wnt signaling pathways (the Wnt proteins) was discovered almost four decades ago by Nüsslein-Volhard and Wieschaus. They identified and originally named the gene "wingless" (wg) after a phenotype discovered during their landmark genetic screen in Drosophila for body pattern defects. At around the same time, another researcher named Harold Varmus found that a murine tumor virus activates the mammalian wg homolog, Int-1, which...
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The transcription factor NF-κB was discovered in 1986 in the lab of Nobel laureate Professor David Baltimore, for its interaction with the immunoglobulin light chain enhancer in B-cells. After more than three decades of study, it is now evident that NF-κB regulates the expression of over 100 genes. Most of these genes play an essential role in the innate and adaptive immune responses as well as the inflammatory responses of animals.
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Refinement-based modeling of the ErbB signaling pathway.

Bogdan Iancu1, Usman Sanwal1, Cristian Gratie1

  • 1Computational Biomodeling Laboratory, Turku Centre for Computer Science, Finland; Department of Computer Science, Åbo Akademi University, Finland.

Computers in Biology and Medicine
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This study introduces model refinement, an efficient method for building large biological models. This approach ensures quantitative accuracy is maintained throughout the model development process.

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Computational modelingErbB signaling pathwayEvent-BInvariantModel constructionODE-Based modelsRefinement

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biochemistry

Background:

  • Constructing large-scale biological models is complex and time-consuming.
  • Iterative model augmentation requires computationally intensive refitting at each step.
  • Existing methods are often inefficient for building comprehensive biological models.

Purpose of the Study:

  • To introduce and demonstrate an efficient model refinement approach for large-scale biological model construction.
  • To present a method that preserves quantitative model fit during augmentation.
  • To build the largest refinement-based biomodel to date.

Main Methods:

  • Utilizing a step-by-step algorithmic refinement procedure.
  • Starting with a validated, smaller literature model.
  • Formally checking the consistency of the initial model.

Main Results:

  • Successfully constructed the largest-ever refinement-based biomodel (421 species, 928 reactions).
  • Demonstrated the preservation of quantitative model fit throughout the refinement process.
  • Validated the efficiency of the model refinement technique.

Conclusions:

  • Model refinement offers an effective alternative to iterative refitting for large biological models.
  • This approach significantly reduces computational cost and time.
  • The developed method enables the creation of larger and more accurate biological models.