The University of Southampton
×
Filter your search:
University of Southampton Institutional Repository

A 1.26µW cytomimetic IC emulating complex nonlinear mammalian cell cycle dynamics: synthesis, simulation and proof-of-concept measured results

A 1.26µW cytomimetic IC emulating complex nonlinear mammalian cell cycle dynamics: synthesis, simulation and proof-of-concept measured results
A 1.26µW cytomimetic IC emulating complex nonlinear mammalian cell cycle dynamics: synthesis, simulation and proof-of-concept measured results
Cytomimetic circuits represent a novel, ultra low-power, continuous-time, continuous-value class of circuits, capable of mapping on silicon cellular and molecular dynamics modelled by means of nonlinear ordinary differential equations (ODEs). Such monolithic circuits are in principle able to emulate on chip, single or multiple cell operations in a highly parallel fashion. Cytomimetic topologies can be synthesized by adopting the Nonlinear Bernoulli Cell Formalism (NBCF), a mathematical framework that exploits the striking similarities between the equations describing weakly-inverted Metal-Oxide Semiconductor (MOS) devices and coupled nonlinear ODEs, typically appearing in models of naturally encountered biochemical systems. The NBCF maps biological state variables onto strictly positive subthreshold MOS circuit currents. This paper presents the synthesis, the simulation and proof-of-concept chip results corresponding to the emulation of a complex cellular network mechanism, the skeleton model for the network of Cyclin-dependent Kinases (CdKs) driving the mammalian cell cycle. This five variable nonlinear biological model, when appropriate model parameter values are assigned, can exhibit multiple oscillatory behaviors, varying from simple periodic oscillations, to complex oscillations such as quasi-periodicity and chaos. The validity of our approach is verified by simulated results with realistic process parameters from the commercially available AMS 0.35 µm technology and by chip measurements. The fabricated chip occupies an area of 2.27 mm2 and consumes a power of 1.26 µW from a power supply of 3 V. The presented cytomimetic topology follows closely the behavior of its biological counterpart, exhibiting similar time-dependent solutions of the Cdk complexes, the transcription factors and the proteins.
biological system modelling, capacitors, computational modelling, integrated circuit modelling, mathematical model, topology, bernoulli cell, cell cycle, cytomimetic circuits, log-domain, nonlinear dynamics, subthreshold MOSFETs, ultra-low power
1932-4545
543-554
Houssein, A.
044959cf-b1f4-41c4-89d7-50618348b6d1
Papadimitriou, K.I.
c0535540-f862-41b1-9cf3-92b1f46a4145
Drakakis, E.M.
87df4781-b386-4d19-b028-53ec21f7b27d
Houssein, A.
044959cf-b1f4-41c4-89d7-50618348b6d1
Papadimitriou, K.I.
c0535540-f862-41b1-9cf3-92b1f46a4145
Drakakis, E.M.
87df4781-b386-4d19-b028-53ec21f7b27d

Houssein, A., Papadimitriou, K.I. and Drakakis, E.M. (2015) A 1.26µW cytomimetic IC emulating complex nonlinear mammalian cell cycle dynamics: synthesis, simulation and proof-of-concept measured results. IEEE Transactions on Biomedical Circuits and Systems, 9 (4), 543-554. (doi:10.1109/TBCAS.2015.2450021).

Record type: Article

Abstract

Cytomimetic circuits represent a novel, ultra low-power, continuous-time, continuous-value class of circuits, capable of mapping on silicon cellular and molecular dynamics modelled by means of nonlinear ordinary differential equations (ODEs). Such monolithic circuits are in principle able to emulate on chip, single or multiple cell operations in a highly parallel fashion. Cytomimetic topologies can be synthesized by adopting the Nonlinear Bernoulli Cell Formalism (NBCF), a mathematical framework that exploits the striking similarities between the equations describing weakly-inverted Metal-Oxide Semiconductor (MOS) devices and coupled nonlinear ODEs, typically appearing in models of naturally encountered biochemical systems. The NBCF maps biological state variables onto strictly positive subthreshold MOS circuit currents. This paper presents the synthesis, the simulation and proof-of-concept chip results corresponding to the emulation of a complex cellular network mechanism, the skeleton model for the network of Cyclin-dependent Kinases (CdKs) driving the mammalian cell cycle. This five variable nonlinear biological model, when appropriate model parameter values are assigned, can exhibit multiple oscillatory behaviors, varying from simple periodic oscillations, to complex oscillations such as quasi-periodicity and chaos. The validity of our approach is verified by simulated results with realistic process parameters from the commercially available AMS 0.35 µm technology and by chip measurements. The fabricated chip occupies an area of 2.27 mm2 and consumes a power of 1.26 µW from a power supply of 3 V. The presented cytomimetic topology follows closely the behavior of its biological counterpart, exhibiting similar time-dependent solutions of the Cdk complexes, the transcription factors and the proteins.

Full text not available from this repository.

More information

Accepted/In Press date: 24 June 2015
Published date: 24 August 2015
Keywords: biological system modelling, capacitors, computational modelling, integrated circuit modelling, mathematical model, topology, bernoulli cell, cell cycle, cytomimetic circuits, log-domain, nonlinear dynamics, subthreshold MOSFETs, ultra-low power
Organisations: Electronics & Computer Science
Learn more about Biomedical Electronics research Learn more about Electronics & Computer Science research

Identifiers

Local EPrints ID: 381400
URI: http://eprints.soton.ac.uk/id/eprint/381400
DOI: doi:10.1109/TBCAS.2015.2450021
ISSN: 1932-4545
PURE UUID: 5fa08980-27c5-413d-a67b-19d2e9a9a271

Catalogue record

Date deposited: 06 Oct 2015 13:24
Last modified: 03 Aug 2018 16:34

Export record

Altmetrics

Contributors

Author: A. Houssein
Author: K.I. Papadimitriou
Author: E.M. Drakakis

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Library staff additional information
Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×