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- Christoph Zechner, Jakob Ruess, +4 authors Heinz Koeppl
- Proceedings of the National Academy of Sciences…
- 2012

Recent computational studies indicate that the molecular noise of a cellular process may be a rich source of information about process dynamics and parameters. However, accessing this source requires stochastic models that are usually difficult to analyze. Therefore, parameter estimation for stochastic systems using distribution measurements, as provided… (More)

- Christian Schilling, Sergiy Bogomolov, Thomas A. Henzinger, Andreas Podelski, Jakob Ruess
- Biosystems
- 2015

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… (More)

- Jakob Ruess, Andreas Milias-Argeitis, John Lygeros
- Journal of the Royal Society, Interface
- 2013

Exploiting the information provided by the molecular noise of a biological process has proved to be valuable in extracting knowledge about the underlying kinetic parameters and sources of variability from single-cell measurements. However, quantifying this additional information a priori, to decide whether a single-cell experiment might be beneficial, is… (More)

- J Ruess, A Milias-Argeitis, S Summers, J Lygeros
- The Journal of chemical physics
- 2011

In stochastic models of chemically reacting systems that contain bimolecular reactions, the dynamics of the moments of order up to n of the species populations do not form a closed system, in the sense that their time-derivatives depend on moments of order n + 1. To close the dynamics, the moments of order n + 1 are generally approximated by nonlinear… (More)

- Jakob Ruess, John Lygeros
- ACM Trans. Model. Comput. Simul.
- 2015

Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of the molecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually… (More)

- Jakob Ruess, Francesca Parise, Andreas Milias-Argeitis, Mustafa Khammash, John Lygeros
- Proceedings of the National Academy of Sciences…
- 2015

Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal… (More)

- Jakob Ruess
- The Journal of chemical physics
- 2015

Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which… (More)

- Jakob Ruess, John Lygeros
- CDC
- 2013

In biochemical reaction networks stochasticity arising from molecular fluctuations often plays an important role. Recent years have seen an increasing number of studies which employed single-cell population experiments and used the measured stochasticity to estimate the parameters of stochastic kinetic models. Currently, there exist two approaches for… (More)

- Francesca Parise, John Lygeros, Jakob Ruess
- Front. Environ. Sci.
- 2015

Mathematical models are of fundamental importance in the understanding of complex population dynamics. For instance, they can be used to predict the population evolution starting from different initial conditions or to test how a system responds to external perturbations. For this analysis to be meaningful in real applications, however, it is of paramount… (More)

- Aron Hjartarson, Jakob Ruess, John Lygeros
- CDC
- 2013