Parameter estimation for biochemical reaction networks using Wasserstein distances
نویسندگان
چکیده
منابع مشابه
Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks
Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and ...
متن کاملNumerical modeling for nonlinear biochemical reaction networks
Nowadays, numerical models have great importance in every field of science, especially for solving the nonlinear differential equations, partial differential equations, biochemical reactions, etc. The total time evolution of the reactant concentrations in the basic enzyme-substrate reaction is simulated by the Runge-Kutta of order four (RK4) and by nonstandard finite difference (NSFD) method. A...
متن کاملAdaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks
Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital import...
متن کاملHistogram Based Segmentation Using Wasserstein Distances
We developed variational models for image segmentation that incorporate histogram information into level set based curve evolution techniques. The novelty is in the use of Wasserstein mass transfer metrics in order to compare histograms; we found that this improves the results significantly over previous Left: Segmentation based on average intensities via ChanVese model. Right: Proposed model b...
متن کاملDistributed Incremental Least Mean-Square for Parameter Estimation using Heterogeneous Adaptive Networks in Unreliable Measurements
Adaptive networks include a set of nodes with adaptation and learning abilities for modeling various types of self-organized and complex activities encountered in the real world. This paper presents the effect of heterogeneously distributed incremental LMS algorithm with ideal links on the quality of unknown parameter estimation. In heterogeneous adaptive networks, a fraction of the nodes, defi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Physics A: Mathematical and Theoretical
سال: 2019
ISSN: 1751-8113,1751-8121
DOI: 10.1088/1751-8121/ab5877