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SUMMARY:Analysis and control of stochastic reaction networks with applicat
 ions to biological networks
DTSTART:20140221T101500
DTSTAMP:20260406T113440Z
UID:80cfe0e160ae00675a329cb697efd01438d232c5f8b775276a308ec7
CATEGORIES:Conferences - Seminars
DESCRIPTION:Corentin Briat\, ETHZ\nBio: Corentin Briat was born in Lannion
 \, France\, in 1982. He received both his Enginner's degree and Master's d
 egree in electrical engineering with specialization in control in 2005 fro
 m the Grenoble Institute of Technology\, Grenoble\, France. He received a 
 PhD degree in systems and control theory from the same university in 2008.
  From 2009 to 2011\, he held an ACCESS postdoctoral position in the ACCESS
  Linnaeus Center at  the Royal Institute of Technology\, Stockholm\, Swed
 en. Since 2012\, he is holding a postdoctoral position in the Department o
 f Biosystems Science and Engineering at the Swiss Federal Institute of Tec
 hnology - Zürich\,  Switzerland. His interests include time-delay system
 s\, robust and LPV control\, hybrid systems\, sampled-data systems\, posit
 ive systems and theoretical problems arising in the modeling\, analysis\, 
 design and  control of communication and biological networks.\nReaction n
 etworks are systems in which the populations of a finite number of species
  evolve according to predefined interactions. Such networks are found as m
 odeling tools in many disciplines (spanning biochemistry\, epidemiology\, 
 pharmacology\, ecology and social networks). Traditionally\, reaction netw
 orks are mathematically analyzed by expressing the dynamics as a set of or
 dinary differential equations. Such a deterministic model is reasonably a
 ccurate when the number of network participants is large. However\, when t
 his is not the case\, the discrete nature of the interactions becomes impo
 rtant and the dynamics is inherently noisy. This random component of the d
 ynamics cannot be ignored as it can have a significant impact on the macr
 oscopic properties of the system. This is the reason why stochastic models
  for reaction networks are necessary for representing certain reaction net
 works. The tools for analyzing them\, however\, still lag far behind their
  deterministic counterparts.\nIn this talk\, a short introduction to biolo
 gical mechanisms and biological problems are first presented via simple ex
 amples in order to set up the ideas. Different modeling techniques will be
  discussed together with their applicability domain\, their benefits and t
 heir drawbacks. A constructive framework for examining the long-term behav
 ior and stability properties of the reaction dynamics in a stochastic sett
 ing is then proposed. In particular\, we will address the problem of deter
 mining ergodicity of the reaction dynamics\, which is analogous to having 
 a globally attracting fixed point for deterministic dynamics.  We will th
 en demonstrate that stability properties of a wide class of networks can b
 e assessed from theoretical results that can be recast as efficient and sc
 alable linear programs\, well-known for their tractability. It is notably 
 shown that the computational complexity is often linear in the number of s
 pecies\, and worst-case quadratic. We illustrate the applicability of the 
 results on several reaction networks arising in fields such as biochemistr
 y\, epidemiology and ecology. Finally\, two control problems will be discu
 ssed. The first one is population control where the aim is to control the 
 average number of proteins present in a cell-population. Variance control 
 can also be performed. This is an example of in-silico control. The second
  one is concerned with single cell control meaning that the goal is to con
 trol the average number of a protein in a single-cell. This is an example 
 of in-vivo control.
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STATUS:CONFIRMED
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