{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T13:10:40Z","timestamp":1740057040283,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011]]},"abstract":"<jats:p>The dynamics of genetic regulatory networks are often affected by stochastic noise, due to the small number of molecules involved in some reactions. The role of these fluctuations is analyzed in a discrete model of gene regulatory networks, i.e. that of noisy random Boolean networks. By relating the asymptotic states of the noisy system to the different cell types, we show how the main features of the important process of cell differentiation can be described by assuming that the noise level changes as differentiation proceeds. Differentiation is seen as a series of transitions from an asymptotic state in which the system can wander among many states under the action of noise to other asymptotic states in which the system can reach fewer and fewer states. This model easily describes the fact that multi-potent cells can stochastically differentiate along various routes. We show here that the process can also be controlled (as it happens in the embryo growth) so that it is possible to determine the final fully differentiated state of the cell. This is achieved by forcing some genes, which are called here &amp;ldquo;swithces&amp;rdquo;, to take constant values, in a way which mimicks the influence of external signals, and by simoultaneously varying the noise level in the cell.<\/jats:p>","DOI":"10.3233\/978-1-60750-692-8-209","type":"book-chapter","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:06:08Z","timestamp":1740053168000},"source":"Crossref","is-referenced-by-count":0,"title":["Cell differentiation in noisy random Boolean network"],"prefix":"10.3233","author":[{"family":"Barbieri A.","sequence":"additional","affiliation":[]},{"family":"Villani M.","sequence":"additional","affiliation":[]},{"family":"Serra R.","sequence":"additional","affiliation":[]},{"family":"Kauffman S.A.","sequence":"additional","affiliation":[]},{"family":"Colacci A.","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Neural Nets WIRN10"],"original-title":[],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:56:07Z","timestamp":1740056167000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=226&spage=209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-60750-692-8-209","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2011]]}}}