HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks
| Autor: | Upinder S. Bhalla |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cell signaling
Bistability Physiology Protein Synthesis Signal transduction Biochemistry Nervous System Reduction (complexity) Medicine and Health Sciences Biology (General) Abstraction (linguistics) computer.programming_language Feedback Physiological Hill differential equation Ecology Chemical Synthesis Signaling cascades Observable Enzymes Electrophysiology Computational Theory and Mathematics Modeling and Simulation Physical Sciences symbols Cellular Structures and Organelles Anatomy Biological system Network Analysis Research Article Optimization Computer and Information Sciences Feedback inhibition MAPK signaling cascades Biosynthetic Techniques QH301-705.5 Neurophysiology Signaling Complexes Research and Analysis Methods Models Biological Cellular and Molecular Neuroscience symbols.namesake Developmental Neuroscience Genetics Molecular Biology Ecology Evolution Behavior and Systematics Ode Feed forward Biology and Life Sciences Proteins Function (mathematics) Cell Biology BCM theory Python (programming language) Signaling Networks Formalism (philosophy of mathematics) Cell Signaling Structures Cellular Neuroscience Synapses Enzymology computer Mathematics Neuroscience Synaptic Plasticity |
| Zdroj: | PLoS Computational Biology, Vol 17, Iss 11, p e1009621 (2021) PLoS Computational Biology |
| ISSN: | 1553-7358 |
| Popis: | Signaling networks mediate many aspects of cellular function. The conventional, mechanistically motivated approach to modeling such networks is through mass-action chemistry, which maps directly to biological entities and facilitates experimental tests and predictions. However such models are complex, need many parameters, and are computationally costly. Here we introduce the HillTau form for signaling models. HillTau retains the direct mapping to biological observables, but it uses far fewer parameters, and is 100 to over 1000 times faster than ODE-based methods. In the HillTau formalism, the steady-state concentration of signaling molecules is approximated by the Hill equation, and the dynamics by a time-course tau. We demonstrate its use in implementing several biochemical motifs, including association, inhibition, feedforward and feedback inhibition, bistability, oscillations, and a synaptic switch obeying the BCM rule. The major use-cases for HillTau are system abstraction, model reduction, scaffolds for data-driven optimization, and fast approximations to complex cellular signaling. Author summary Chemical signals mediate many computations in cells, from housekeeping functions in all cells to memory and pattern selectivity in neurons. These signals form complex networks of interactions. Computer models are a powerful way to study how such networks behave, but it is hard to get all the chemical details for typical models, and it is slow to run them with standard numerical approaches to chemical kinetics. We introduce HillTau as a simplified way to model complex chemical networks. HillTau models condense multiple reaction steps into single steps defined by a small number of parameters for activation and settling time. As a result the models are simple, easy to find values for, and they run quickly. Remarkably, they fit the full chemical formulations rather well. We illustrate the utility of HillTau for modeling several signaling network functions, and for fitting complicated signaling networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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