
@article {150949,
	title = {Mathematical basis and toolchain for hierarchical optimization of biochemical networks.},
	journal = {PLoS Comput Biol},
	volume = {20},
	year = {2024},
	month = {2024 Dec 02},
	pages = {e1012624},
	abstract = {<p>Biological signalling systems are complex, and efforts to build mechanistic models must confront a huge parameter space, indirect and sparse data, and frequently encounter multiscale and multiphysics phenomena. We present HOSS, a framework for Hierarchical Optimization of Systems Simulations, to address such problems. HOSS operates by breaking down extensive systems models into individual pathway blocks organized in a nested hierarchy. At the first level, dependencies are solely on signalling inputs, and subsequent levels rely only on the preceding ones. We demonstrate that each independent pathway in every level can be efficiently optimized. Once optimized, its parameters are held constant while the pathway serves as input for succeeding levels. We develop an algorithmic approach to identify the necessary nested hierarchies for the application of HOSS in any given biochemical network. Furthermore, we devise two parallelizable variants that generate numerous model instances using stochastic scrambling of parameters during initial and intermediate stages of optimization. Our results indicate that these variants produce superior models and offer an estimate of solution degeneracy. Additionally, we showcase the effectiveness of the optimization methods for both abstracted, event-based simulations and ODE-based models.</p>
},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1012624},
	author = {Viswan, Nisha Ann and Tribut, Alexandre and Gasparyan, Manvel and Radulescu, Ovidiu and Bhalla, Upinder S}
}
@article {136527,
	title = {Understanding molecular signaling cascades in neural disease using multi-resolution models.},
	journal = {Curr Opin Neurobiol},
	volume = {83},
	year = {2023},
	month = {2023 Nov 14},
	pages = {102808},
	abstract = {<p>If the genome defines the program for the operations of a cell, signaling networks execute it. These cascades of chemical, cell-biological, structural, and trafficking events span milliseconds (e.g., synaptic release) to potentially a lifetime (e.g., stabilization of dendritic spines). In principle almost every aspect of neuronal function, particularly at the synapse, depends on signaling. Thus dysfunction of these cascades, whether through mutations, local dysregulation, or infection, leads to disease. The sheer complexity of these pathways is matched by the range of diseases and the diversity of their phenotypes. In this review, we discuss how to build computational models, how these models are essential to tackle this complexity, and the benefits of using families of models at different levels of detail to understand signaling in health and disease.</p>
},
	issn = {1873-6882},
	doi = {10.1016/j.conb.2023.102808},
	author = {Viswan, Nisha Ann and Bhalla, Upinder Singh}
}
