The internal dynamics of a living cell i s generally very noisy. An important source of th is noise is the intermittency of reactions among v arious molecular species in the cell. The role of this noise is commonly studied using stochastic models for reaction networks\, where the dynamics is described by a continuous-time Markov chain wh ose states represent the molecular counts of vari ous species. In this talk we will discuss how the long-term behavior of such Markov chains can be a ssessed using a blend of ideas from probability th eory\, linear algebra and \;optimisation \;theory. In particular we will describe how many biomolecular networks can be viewed as generalise d birth-death networks\, which leads to a simple computational framework for determining their stab ility properties such as ergodicity and convergen ce of moments. We demonstrate the wide-applicabil ity of our framework using many examples from Syst ems and Synthetic Biology. We also discuss how ou r results can hel p in analysing regulatory circu its within cells and in understanding the entrainm ent properties of noisy biomolecular oscillators. LOCATION:Seminar Room 1\, Newton Institute CONTACT:INI IT END:VEVENT END:VCALENDAR