Browsing by Author "Bhalla, Upinder S."

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    Minimum Information About a Simulation Experiment (MIASE)
    Waltemath, Dagmar; Adams, Richard; Beard, Daniel A.; Bergmann, Frank T.; Bhalla, Upinder S.; Britten, Randall; Chelliah, Vijayalakshmi; Cooling, Michael T.; Cooper, Jonathan; Crampin, Edmund J.; Garny, Alan; Hoops, Stefan; Hucka, Michael; Hunger, Peter; Klipp, Edda; Laibe, Camille; Miller, Andrew K.; Moraru, Ion; Nickerson, David; Nielsen, Poul; Nikolski, Macha; Sahle, Sven; Sauro, Herbert M.; Schmidt, Henning; Snoep, Jacky L.; Tolle, Dominic; Wolkenhauer, Olaf; Le Novère, Nicolas (Public Library of Science, 2011-04-28)
    Reproducibility of experiments is a basic requirement for science. Minimum Information (MI) guidelines have proved a helpful means of enabling reuse of existing work in modern biology. The Minimum Information Required in the Annotation of Models (MIRIAM) guidelines promote the exchange and reuse of biochemical computational models. However, information about a model alone is not sufficient to enable its efficient reuse in a computational setting. Advanced numerical algorithms and complex modeling workflows used in modern computational biology make reproduction of simulations difficult. It is therefore essential to define the core information necessary to perform simulations of those models. The Minimum Information About a Simulation Experiment (MIASE, Glossary in Box 1) describes the minimal set of information that must be provided to make the description of a simulation experiment available to others. It includes the list of models to use and their modifications, all the simulation procedures to apply and in which order, the processing of the raw numerical results, and the description of the final output. MIASE allows for the reproduction of any simulation experiment. The provision of this information, along with a set of required models, guarantees that the simulation experiment represents the intention of the original authors. Following MIASE guidelines will thus improve the quality of scientific reporting, and will also allow collaborative, more distributed efforts in computational modeling and simulation of biological processes.
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    Using sensitivity analyses to understand bistable system behavior
    Sreedharan, Vandana; Bhalla, Upinder S.; Ramakrishnan, Naren (2023-04-06)
    Background Bistable systems, i.e., systems that exhibit two stable steady states, are of particular interest in biology. They can implement binary cellular decision making, e.g., in pathways for cellular differentiation and cell cycle regulation. The onset of cancer, prion diseases, and neurodegenerative diseases are known to be associated with malfunctioning bistable systems. Exploring and characterizing parameter spaces in bistable systems, so that they retain or lose bistability, is part of a lot of therapeutic research such as cancer pharmacology. Results We use eigenvalue sensitivity analysis and stable state separation sensitivity analysis to understand bistable system behaviors, and to characterize the most sensitive parameters of a bistable system. While eigenvalue sensitivity analysis is an established technique in engineering disciplines, it has not been frequently used to study biological systems. We demonstrate the utility of these approaches on a published bistable system. We also illustrate scalability and generalizability of these methods to larger bistable systems. Conclusions Eigenvalue sensitivity analysis and separation sensitivity analysis prove to be promising tools to define parameter design rules to make switching decisions between either stable steady state of a bistable system and a corresponding monostable state after bifurcation. These rules were applied to the smallest two-component bistable system and results were validated analytically. We showed that with multiple parameter settings of the same bistable system, we can design switching to a desirable state to retain or lose bistability when the most sensitive parameter is varied according to our parameter perturbation recommendations. We propose eigenvalue and stable state separation sensitivity analyses as a framework to evaluate large and complex bistable systems.