1 citations · 2 across the 4 of their papers we have counts for
6 papers
Deep Reinforcement Learning for Controlled Traversing of the Attractor Landscape of Boolean Models in the Context of Cellular Reprogramming
Andrzej Mizera, Jakub Zarzycki
Cellular reprogramming can be used for both the prevention and cure of different diseases. However, the efficiency of discovering reprogramming strategies with classical wet-lab ex…
A Decomposition-based Approach towards the Control of Boolean Networks (Technical Report)
Soumya Paul, Cui Su, Jun Pang +1
We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be controlled to drive its dynamics from an initial steady state (o…
Taming Asynchrony for Attractor Detection in Large Boolean Networks (Technical Report)
Andrzej Mizera, Jun Pang, Hongyang Qu +1
Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analysing a Boolean network is to identify all the attractors. This becomes…
Fast Simulation of Probabilistic Boolean Networks (Technical Report)
Andrzej Mizera, Jun Pang, Qixia Yuan
Probabilistic Boolean networks (PBNs) is an important mathematical framework widely used for modelling and analysing biological systems. PBNs are suited for modelling large biologi…
Parallel Approximate Steady-state Analysis of Large Probabilistic Boolean Networks (Technical Report)
Andrzej Mizera, Jun Pang, Qixia Yuan
Probabilistic Boolean networks (PBNs) is a widely used computational framework for modelling biological systems. The steady-state dynamics of PBNs is of special interest in the ana…
Reviving the Two-state Markov Chain Approach (Technical Report)
Andrzej Mizera, Jun Pang, Qixia Yuan
Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in…