Partial Evaluation via Code Generation for Static Stochastic Reaction Network Models

Author(s):  
Till Köster ◽  
Tom Warnke ◽  
Adelinde M. Uhrmacher
2018 ◽  
Author(s):  
Corentin Briat ◽  
Mustafa Khammash

AbstractDelays are important phenomena arising in a wide variety of real world systems, including biological ones, because of diffusion/propagation effects or as simplifying modeling elements. We propose here to consider delayed stochastic reaction networks, a class of networks that has been relatively few studied until now. The difficulty in analyzing them resides in the fact that their state-space is infinite-dimensional. We demonstrate here that by restricting the delays to be phase-type distributed, one can represent the associated delayed reaction network as a reaction network with finite-dimensional state-space. This can be achieved by suitably adding chemical species and reactions to the delay-free network following a simple algorithm which is fully characterized. Since phase-type distributions are dense in the set of probability distributions, they can approximate any distribution arbitrarily closely and this makes their consideration only a bit restrictive. As the state-space remains finite-dimensional, usual tools developed for non-delayed reaction network directly apply. In particular, we prove, for unimolecular mass-action reaction networks, that the delayed stochastic reaction network is ergodic if and only if the delay-free network is ergodic as well. Bimolecular reactions are more difficult to consider but slightly stronger analogous results are nevertheless obtained. These results demonstrate that delays have little to no harm to the ergodicity property of reaction networks as long as the delays are phase-type distributed, and this holds regardless the complexity of their distribution. We also prove that the presence of those delays adds convolution terms in the moment equation but does not change the value of the stationary means compared to the delay-free case. The covariance, however, is influenced by the presence of the delays. Finally, the control of a certain class of delayed stochastic reaction network using a delayed antithetic integral controller is considered. It is proven that this controller achieves its goal provided that the delay-free network satisfy the conditions of ergodicity and output-controllability.


Author(s):  
Tabea Waizmann ◽  
Luca Bortolussi ◽  
Andrea Vandin ◽  
Mirco Tribastone

Stochastic reaction networks are a fundamental model to describe interactions between species where random fluctuations are relevant. The master equation provides the evolution of the probability distribution across the discrete state space consisting of vectors of population counts for each species. However, since its exact solution is often elusive, several analytical approximations have been proposed. The deterministic rate equation (DRE) gives a macroscopic approximation as a compact system of differential equations that estimate the average populations for each species, but it may be inaccurate in the case of nonlinear interaction dynamics. Here we propose finite-state expansion (FSE), an analytical method mediating between the microscopic and the macroscopic interpretations of a stochastic reaction network by coupling the master equation dynamics of a chosen subset of the discrete state space with the mean population dynamics of the DRE. An algorithm translates a network into an expanded one where each discrete state is represented as a further distinct species. This translation exactly preserves the stochastic dynamics, but the DRE of the expanded network can be interpreted as a correction to the original one. The effectiveness of FSE is demonstrated in models that challenge state-of-the-art techniques due to intrinsic noise, multi-scale populations and multi-stability.


BIOMATH ◽  
2016 ◽  
Vol 5 (1) ◽  
pp. 1607311 ◽  
Author(s):  
Svetoslav Marinov Markov

In this work we  discuss some methodological aspects of the creation and formulation of mathematical  models describing the growth of species from the point of view of reaction kinetics. Our discussion is based on familiar examples of growth models such as logistic growth and enzyme kinetics. We   propose several reaction network  models  for  the amiloid fibrillation processes in the citoplasm. The solutions of the models are sigmoidal functions graphically visualized using  the computer algebra system   Mathematica.


2007 ◽  
Vol 40 (5) ◽  
pp. 225-230 ◽  
Author(s):  
S.C. Burnham ◽  
M.J. Willis ◽  
A.R Wright

Processes ◽  
2018 ◽  
Vol 6 (9) ◽  
pp. 136 ◽  
Author(s):  
Eugenio Cinquemani

Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems. However, general results are lacking, and the advantage over deterministic, population-average approaches has not been explored for network reconstruction. In this work, we study identifiability and propose new reconstruction methods for biochemical interaction networks. Focusing on population-snapshot data and networks with reaction rates affine in the state, for parameter estimation, we derive general methods to test structural identifiability and demonstrate them in connection with practical identifiability for a reporter gene in silico case study. In the same framework, we next develop a two-step approach to the reconstruction of unknown networks of interactions. We apply it to compare the achievable network reconstruction performance in a deterministic and a stochastic setting, showing the advantage of the latter, and demonstrate it on population-snapshot data from a simulated example.


2002 ◽  
Vol 9 (33) ◽  
Author(s):  
Vincent Balat ◽  
Olivier Danvy

We use a code generator--type-directed partial evaluation--to verify conversions between isomorphic types, or more precisely to verify that a composite function is the identity function at some complicated type. A typed functional language such as ML provides a natural support to express the functions and type-directed partial evaluation provides a convenient setting to obtain the normal form of their composition. However, off-the-shelf type-directed partial evaluation turns out to yield gigantic normal forms.<br /> <br />We identify that this gigantism is due to redundancies, and that these redundancies originate in the handling of sums, which uses delimited continuations. We successfully eliminate these redundancies by extending type-directed partial evaluation with memoization capabilities. The result only works for pure functional programs, but it provides an unexpected use of code generation and it yields orders-of-magnitude improvements both in time and in space for type isomorphisms.


2019 ◽  
Vol 7 ◽  
Author(s):  
Rachel M. Wilson ◽  
Rebecca B. Neumann ◽  
Kelsey B. Crossen ◽  
Nicole M. Raab ◽  
Suzanne B. Hodgkins ◽  
...  

2015 ◽  
Vol 11 (A29B) ◽  
pp. 380-384
Author(s):  
Yuri Aikawa

AbstractWe will review the chemical reaction network models of water and its D/H ratio coupled with the dynamics of star formation. Infrared observations show that water ice is abundant even in molecular clouds with relatively low visual extinction (~ 3 mag), which indicates that water ice is formed in early stage of molecular clouds. We thus start from a possible formation site of molecular clouds, i.e. the converging flow of diffuse gas. Then we proceed to dense cloud cores and its gravitational collapse, during which a significant deuterium enrichment occurs. The gas and ice accrete onto the circumstellar disks, which evolve to protoplanetary disks in T Tauri phase. If the disks are turbulent, water could be photodissociated in the disk surface and re-formed in deeper layers. The cycle continues until the dust grains with ice mantle are decoupled from the turbulence and settle to the midplane. The water D/H ratio could thus vary within the disk.


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