Publications by authors named "Cristian Gratie"
The construction of large scale biological models is a laborious task, which is often addressed by adopting iterative routines for model augmentation, adding certain details to an initial high level abstraction of the biological phenomenon of interest. Refitting a model at every step of its development is time consuming and computationally intensive. The concept of model refinement brings about an effective alternative by providing adequate parameter values that ensure the preservation of its quantitative fit at every refinement step.
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- Computational analysis of molecular interaction networks can lead to new treatments for systemic diseases like cancer by helping understand and manipulate these bio-medical networks.
- Recent advancements in network control theory have revealed that it can effectively address the challenges of controlling these networks, including a polynomial algorithm developed in 2011 by Liu et al. and further generalization by Gao et al. in 2014.
- This study proves that the target controllability problem is NP-hard, meaning it's very complex to solve, and offers improvements to Gao et al.'s algorithm, resulting in faster execution times and smaller solution sets.
Control theory is a well-established approach in network science, with applications in bio-medicine and cancer research. We build on recent results for structural controllability of directed networks, which identifies a set of driver nodes able to control an a-priori defined part of the network. We develop a novel and efficient approach for the (targeted) structural controllability of cancer networks and demonstrate it for the analysis of breast, pancreatic, and ovarian cancer.
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