MODEL PREDICTIVE CONTROL OF CANCER CELLULAR DYNAMICS: A NEW STRATEGY FOR THERAPY DESIGN

Benjamin Smart; Irene de Cesare; Ludovic Renson; Lucia Marucci

doi:10.1101/2022.03.14.484268

MODEL PREDICTIVE CONTROL OF CANCER CELLULAR DYNAMICS: A NEW STRATEGY FOR THERAPY DESIGN

Benjamin Smart, Irene de Cesare, Ludovic Renson, Lucia Marucci

Cancer

Research output: Other contribution

Abstract

Recent advancements in Cybergenetics have led to the development of new computational and experimental platforms that enable to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that adaptive model predictive control (MPC) can effectively be used to steer signalling dynamics in Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild-type cells, and to support the design of combination therapies. Our optimisation-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure, minimising both drug-induced toxicity and resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells for drug combination therapy design.

Original language	English
DOIs	https://doi.org/10.1101/2022.03.14.484268
Publication status	Published - 16 Mar 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research Groups and Themes

Bristol BioDesign Institute

Access to Document

10.1101/2022.03.14.484268

Handle.net

Persistent link

Cite this

@misc{823c00cc41d747f69fcd07fdefa8411f,

title = "MODEL PREDICTIVE CONTROL OF CANCER CELLULAR DYNAMICS: A NEW STRATEGY FOR THERAPY DESIGN",

abstract = "Recent advancements in Cybergenetics have led to the development of new computational and experimental platforms that enable to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that adaptive model predictive control (MPC) can effectively be used to steer signalling dynamics in Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild-type cells, and to support the design of combination therapies. Our optimisation-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure, minimising both drug-induced toxicity and resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells for drug combination therapy design.",

author = "Benjamin Smart and Cesare, \{Irene de\} and Ludovic Renson and Lucia Marucci",

year = "2022",

month = mar,

day = "16",

doi = "10.1101/2022.03.14.484268",

language = "English",

type = "Other",

}

TY - GEN

T1 - MODEL PREDICTIVE CONTROL OF CANCER CELLULAR DYNAMICS

T2 - A NEW STRATEGY FOR THERAPY DESIGN

AU - Smart, Benjamin

AU - Cesare, Irene de

AU - Renson, Ludovic

AU - Marucci, Lucia

PY - 2022/3/16

Y1 - 2022/3/16

N2 - Recent advancements in Cybergenetics have led to the development of new computational and experimental platforms that enable to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that adaptive model predictive control (MPC) can effectively be used to steer signalling dynamics in Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild-type cells, and to support the design of combination therapies. Our optimisation-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure, minimising both drug-induced toxicity and resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells for drug combination therapy design.

AB - Recent advancements in Cybergenetics have led to the development of new computational and experimental platforms that enable to robustly steer cellular dynamics by applying external feedback control. Such technologies have never been applied to regulate intracellular dynamics of cancer cells. Here, we show in silico that adaptive model predictive control (MPC) can effectively be used to steer signalling dynamics in Non-Small Cell Lung Cancer (NSCLC) cells to resemble those of wild-type cells, and to support the design of combination therapies. Our optimisation-based control algorithm enables tailoring the cost function to force the controller to alternate different drugs and/or reduce drug exposure, minimising both drug-induced toxicity and resistance to treatment. Our results pave the way for new cybergenetics experiments in cancer cells for drug combination therapy design.

UR - https://doi.org/10.1101/2022.03.14.484268

U2 - 10.1101/2022.03.14.484268

DO - 10.1101/2022.03.14.484268

M3 - Other contribution

ER -

MODEL PREDICTIVE CONTROL OF CANCER CELLULAR DYNAMICS: A NEW STRATEGY FOR THERAPY DESIGN

Abstract

UN SDGs

Research Groups and Themes

Access to Document

Handle.net

Other files and links

Fingerprint

Cite this