1 citations · 1 across the 1 of their papers we have counts for
5 papers
Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski +1
The moments of spatial probabilistic systems are often given by an infinite hierarchy of coupled differential equations. Moment closure methods are used to approximate a subset of…
MCell-R: A particle-resolution network-free spatial modeling framework
Jose-Juan Tapia, Ali Sinan Saglam, Jacob Czech +4
Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed…
Spatial Stochastic Modeling with MCell and CellBlender
Sanjana Gupta, Jacob Czech, Robert Kuczewski +4
This chapter provides a brief introduction to the theory and practice of spatial stochastic simulations. It begins with an overview of different methods available for biochemical s…
Reference Type Logic Variables in Constraint-logic Object-oriented Programming
Jan C. Dageförde
Constraint-logic object-oriented programming, for example using Muli, facilitates the integrated development of business software that occasionally involves finding solutions to co…
Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics
Oliver K. Ernst, Thomas Bartol, Terrence Sejnowski +1
Finding reduced models of spatially-distributed chemical reaction networks requires an estimation of which effective dynamics are relevant. We propose a machine learning approach t…