2026-09-20T16:57:56Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/823962022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Domitilla Del Vecchio.
Herath, Narmada Kumari
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2013-11-18T19:18:00Z
2013-11-18T19:18:00Z
2013
2013
http://hdl.handle.net/1721.1/82396
862110053
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 63-64).
In an interconnection of two components in a bio-molecular network, noise in the downstream component can be reduced by increasing the magnitude of the down-stream signal. However, this method of reducing noise increases the back-effect to the upstream system, called 'retroactivity', thereby increasing the perturbation to the upstream system. In this thesis, we seek to quantify the total error in the system caused by the perturbations due to retroactivity and noise, and to analyze the trade-off between the two errors. We model the system as a set of non-linear chemical Langevin equations and quantify the trade-off for two different approximations of this non-linear model. First we consider a system linearized about a fixed point and quantify the trade-off using transfer functions. Next we use a linear approximation of the propensity functions in the Langevin equation and quantify the error by calculating upper bounds using contraction theory for deterministic and stochastic systems. Future research directions in improving the upper bounds are discussed.
by Narmada Kumari Herath.
S.M.
64 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Fundamental limitations between noise and back-action in bio-molecular networks
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="96deb204-5452-435a-875f-24b32c1123b8">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Fundamental limitations between noise and back-action in bio-molecular networks</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2013</PublicationDate>
<Authors>
<Author>
<DisplayName>Herath, Narmada Kumari</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>In an interconnection of two components in a bio-molecular network, noise in the downstream component can be reduced by increasing the magnitude of the down-stream signal. However, this method of reducing noise increases the back-effect to the upstream system, called 'retroactivity', thereby increasing the perturbation to the upstream system. In this thesis, we seek to quantify the total error in the system caused by the perturbations due to retroactivity and noise, and to analyze the trade-off between the two errors. We model the system as a set of non-linear chemical Langevin equations and quantify the trade-off for two different approximations of this non-linear model. First we consider a system linearized about a fixed point and quantify the trade-off using transfer functions. Next we use a linear approximation of the propensity functions in the Langevin equation and quantify the error by calculating upper bounds using contraction theory for deterministic and stochastic systems. Future research directions in improving the upper bounds are discussed.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>