Project
AA1-8
Random Bifurcations in Chemical Reaction Networks
Project Heads
Maximilian Engel
Project Members
Guillermo Olicón Méndez
Project Duration
01.10.2021 – 30.09.2023
Located at
FU Berlin
Description
In this project, we develop a random bifurcation theory for chemical reaction networks whose kinetics are properly modeled by Markov jump processes. For large reactants, model reduction to reaction rate equations (ODEs) and Langevin equations (SDEs) is possible and computationally alleviating. We investigate which model reductions succeed in preserving pathwise properties of the jump process in terms of dynamical stability and bifurcation behavior stemming from varying reaction rates.
In more details, we consider general models of large sets of molecules that can be allocated to a certain number of different species. Reactions, changing the number of molecules of a subset of species, occur at certain rates which are defined as propensity funtions involving reaction rate constants. Assuming that these functions depend only on the state of the whole reaction system, one can describe the changes of the system over time, i.e. the dynamics, as a Markov jump process driven by independent Poisson processes. For high accumulations of reaction events, the Poisson variables can be approximated by normal random variables, yielding the chemical Langevin equation (CLE) which is a stochastic differential equation (SDE). In the thermodynamic limit where volume and volume-dependent molecule numbers scale in a fixed proportion approaching infinity, we may average over the noise terms to obtain the purely deterministic version of the CLE, i.e. an ODE also called reaction rate equation (RRE).
For all three approximation levels, there are corresponding differential equations, describing the evolution of the probability distribution of states ; in the stochastic case, this evolution is described by the forward Kolmogorov equation, which takes the form of the chemical master equation for the Markov jump process and the form of a Fokker-Planck equation for the CLE. The corresponding PDE for the RRE is called Liouville equation. Many investigations focus on these equations comparing distributions of the reaction process and its approximation models, and by that only capture statistical properties of the processes.
We employ the perspective of random dynamical systems (RDS) in order to understand trajectory-wise behavior of the Markov jump process in comparison to the SDE solution, and to demonstrate what can be missed, also in the thermodynamic limit, by only focusing on expectations given by solutions of the RRE. The RDS approach compares two trajectories with the same noise realizations and analyzes whether they synchronize or diverge asymptotically, determining the form of a random attractor. By applying techniques of linearization and multiplicative ergodic theory, we are the first to investigate and compare thoroughly the stability behavior of all three models; this becomes highly relevant when changes of rate constants are considered such that bifurcations can be detected. A crucial role is played by the Hopf bifurcation, where an attracting equilibrium loses stability and an attracting periodic orbit occurs, which exhibits complicated behavior under noise perturbations (see first Figure below).
One first project concerns the Brusselator, a chemical toy model, for which we investigate transient and asymptotic phenomena in the Langevin dynamics (see several Figures below), building up tools for new RDS descriptions of stochastic oscillators in chemical reaction networks (CRNs). A second current project investigates synchronization effects for underlying Markov jump process in CRNs, in collaboration with S. Winkelmann’s group at ZIB. Both lines of study are anticipated to lead to preprints in the first half of 2022.
Project Webpages
Link to Junior Research Group Website https://www.mi.fu-berlin.de/en/math/groups/ag-random-dynamics/index.html
Selected Publications
R. Chemnitz, M. Engel, G. Olicón-Méndez. Random attractors on countable state spaces, 2024, arXiv:2405.19898v1
M. Engel, G. Olicón-Méndez. Singular perturbation analysis of the Brusselator, 2023, arXiv:2311.00575v3
M. Engel, G. Olicón-Méndez. Noise-induced instabilities in a stochastic Brusselator, accepted for Contemporary Mathematics: topics in multiple time scale dynamics, 2023, arXiv:2305.18021
M. Engel, G. Olicón-Méndez, N. Unger, and S. Winkelmann. Synchronization and random attractors for reaction jump processes, Journal of Dynamics and Differential Equations (2024), DOI.
M. Breden, M. Engel. Computer-assisted proof of shear-induced chaos in stochastically perturbed Hopf systems, accepted for publication in Annals of Applied Probability, 2022, pp. 1-42, arXiv:2101.01491.
A. Viennet, N. Vercauteren, M. Engel, D. Faranda. Guidelines for data-driven approaches to study transitions in multiscale systems: the case of Lyapunov vectors, Chaos 32(11): 113145, 2022, DOI.
M. Engel, C. Kuehn. A random dynamical systems perspective on isochronicity for stochastic oscillations, Communications in Mathematical Physics 386(3): 1603-1641, 2021, DOI.
Selected Pictures
Chaotic random attractor from a stochastic Hopf bifurcation
Such a fractal-like attractor with chaotic properties can occur in stochastically perturbed systems (CLE) that experience a Hopf bifurcation in the deterministic case (RRE), for example in a typical biological modle like the so-called Brusselator.
Synchronization of stochastic trajectories driven by the same noise
Synchonization of trajectories, i.e. the random attractor shrinking to a point, is the other typical behavior of random dynamical systems, also occurring in Hopf bifurcations under the influence of noise.
A stochastic Brusselator
When studied from the RRE perspective (a completely deterministic point of view), the Brusselator exhibits the emergence of an attracting limit cycle for certain values of the system’s parameters.
For sufficiently large values of the system’s volume, the chemical Langevin equations may be studied. However, typically this approach does not admit solutions for all future times.
Instead, we consider the RRE equations but assuming that one parameter is perturbed by brownian noise. This system shares similarities with its deterministic counterpart. For instance, there is still an oscillatory behaviour reminiscent of the RRE formulation.
Finite time instabilities
The random dynamical systems perspective consists of fixing a noise realisation, and exploring how do nearby initial conditions evolve through time. In particular, we can study whether the orbits approach to each other (i.e. they synchronise) or deviate. Our novel approach studies this behaviour in finite-time windows, as opposed to the classical approach where the asymptotic long time behaviour is considered. While orbits may synchronise in the long run, they can exhibit finite-time instabilities beforehand by showing repeated large separations
Finite-Time Lyapunov exponents
Covariant Lyapunov Vectors (CLV) indicate special directions along which nearby orbits exponentially expand away or contract towards each other. The exponential rates at which nearby orbits expand away or contract towards each other in a certain time window are called Finite-Time Lyapunov Exponents (FTLE). A positive sign in FTLEs indicates local exponential deviation of nearby trajectories, while a negative sign indicates the opposite.
These tools from (random) dynamical systems have proven to be useful in all sorts of applications, including those involving real-world data. For chemical reaction networks one can exhibit a landscape of finite-time attraction and repulsion throughout the state space, which helps to understand regions of high finite-time instabilities, qualitative changes in finite-time behaviour, etc.
The Brusselator as a slow-fast system
We propose that a mechanism for noise-induced instabilities for the Brusselator is originated by the combination of the noisy perturbations and a time scale separation of the system into fast and slow regimes. The noisy perturbations may kick the orbits out of the slow regime into the fast one, creating a large separation while the other orbit still remains in the slow regime.
Time-delayed synchronisation in Markov Jump Processess
A Markov Jump Process formulation is sometimes preferred, when modelling the microscopic interactions of chemical reaction networks. This perspective is somewhat numerically demanding and very often overlooked.
These types of processes exhibit interesting behaviours. In particular, under the same noisy perturbations two different orbits can synchronise with a “time lag”.
We develop theoretical and numerical studies in order to understand delay synchronisation in Markov Jump Processes. These are done in collaboration with Stefanie Winkelmann and Nathalie Unger from ZIB.
Outreach
We represented MATH+ in the Science Slam of the Berlin Clusters of Excellence for the Berlin Science Week, in the 2022 and 2023 editions. We presented our research to a general audience at the Roadrunner’s Paradise and Säälchen in Holzmarkt, respectively, getting 2nd place for the night in both occasions.
See the video of the full 2023 edition here!
(Photo by ©SCIoI/Felix Noak )
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