This paper presents the Neural Network Verification (NNV) software tool, a set-based
verification framework for deep neural networks (DNNs) and learning-enabled cyber …

We consider the problem of synthesising rate parameters for stochastic biochemical
networks so that a given time-bounded CSL property is guaranteed to hold, or, in the case of …

The time-evolution of continuous-time discrete-state biochemical processes is governed by
the Chemical Master Equation (CME), which describes the probability of the molecular …

Quantitative mechanistic models are valuable tools for disentangling biochemical pathways
and for achieving a comprehensive understanding of biological systems. However, to be …

We address the problem of simulating biochemical reaction networks with time-dependent
rates and propose a new algorithm based on our rejection-based stochastic simulation …

Gene expression, signal transduction and many other cellular processes are subject to
stochastic fluctuations. The analysis of these stochastic chemical kinetics is important for …

Background Discrete-state stochastic models have become a well-established approach to
describe biochemical reaction networks that are influenced by the inherent randomness of …

Exact stochastic simulation is an indispensable tool for a quantitative study of biochemical
reaction networks. The simulation realizes the time evolution of the model by randomly …

After being considered as a nuisance to be filtered out, it became recently clear that
biochemical noise plays a complex role, often fully functional, for a biomolecular network …

We propose a numerical technique for parameter inference in Markov models of biological
processes. Based on time-series data of a process we estimate the kinetic rate constants by …