Biology subjects

Renson, L.

Publications and source records attributed to Renson, L..

5 recordsLinked to original sources

bioRxiv2024

Embedded Extremum Seeking: A Bio-synthetic Optimiser

In cybergentics, internal control uses synthetic genetic circuits to bypass the sensing and actuation limits of external control. Currently, embedded controllers are limited to basic controllers, like PID. Here, we introduce the Embedded Extremum Seeking Controller, the first example of a bio-synthetic optimiser. Extremum Seeking combines an integral and oscillator circuit to adjust system parameters and achieve a desired optimal steady state. This system uses an integral circuit and oscillator that already exist in synthetic biology. The Extremum Seeker is demonstrated in silico on a Labour Division system, where the controller tunes a kinematic parameter, optimising the ratio of the culture subpopulations to maximise production. This controller paves the way for embedded controllers that precisely direct cellular behaviour without needing external actuation and sensing.

synthetic biology↗

bioRxiv2024

Kernel Filter-Based Adaptive Controllers For Cybergenetics Applications

Cybergenetics is an advancing field that seeks to implement control theory within biological systems. When applying feedback control for the regulation of gene expression or cell proliferation, model-based control strategies can be applied; in this context, online adaptive mathematical models can be used to keep models in tune with the current behaviour of the biological system. Controllers are often constrained by their sampling rate, which is usually relatively low when using microfluidics/microscopy platforms. Current adaptive filters can lead to an inaccurate predictive model when operating with a low sampling rate, leading to sub-optimal control. Here, we propose a kernel filter that can fit model parameters online to produce a more accurate predictive model that can be included within an adaptive model predictive control scheme. The use of the kernel filter is demonstrated in in silico and in vitro experiments, where we control a synthetic gene oscillator and a P53 oscillator, and observe a synthetic toggle switch. Our results show that the kernel filter outperforms a particle filter when used for parameter estimation in both the predictive model accuracy and when included within an adaptive model-based controller.

systems biology↗

bioRxiv2022

EXTERNAL CONTROL OF CANCER CELLULAR DYNAMICS USING ADAPTIVE MODEL PREDICTIVE CONTROL

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.

systems biology↗

bioRxiv2021

Control-based continuation: a new approach to prototype synthetic gene networks

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modelling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch which includes additive process noise and exhibits bistability. We compare results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of a real experiment using an agent-based simulator describing cells growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

synthetic biology↗

bioRxiv2019

Exploring the Dynamics of Nonlinear Biochemical Systems using Control-Based Continuation

AO_SCPLOWBSTRACTC_SCPLOWMathematical modelling is routinely used in Systems Biology to understand the mechanisms causing nonlinear phenomena in gene expression, such as switch-like behaviours and temporal oscillations. The reliability of model predictions and bifurcation analysis depend on modelling assumptions and specific choices of model parameters; however, the identification of models is highly challenging due to the complexity of biochemical interactions and noise in experimental data. This paper numerically investigates the use of control-based continuation (CBC) for tracking dynamical features of biochemical systems and, in particular, the bistable dynamics of a gene regulating pluripotency in embryonic stem cells. CBC is a method that exploits feedback control and path following algorithms to explore the dynamic features of a nonlinear physical system directly during experimental tests. CBC applications have so far been limited to non-living (i.e. electro-mechanical) systems. Our numerical simulations show that, in principle, CBC could also be applied to biological experiments to characterise the switch-like dynamics of genes that are important for cell decision making.

systems biology↗