Engineering transient dynamics of artificial cells by stochastic distribution of enzymes

AbstractRandom fluctuations are inherent to all complex molecular systems. Although nature has evolved mechanisms to control stochastic events to achieve the desired biological output, reproducing this in synthetic systems represents a significant challenge. Here we present an artificial platform that enables us to exploit stochasticity to direct motile behavior. We found that enzymes, when confined to the fluidic polymer membrane of a core-shell coacervate, were distributed stochastically in time and space. This resulted in a transient, asymmetric configuration of propulsive units, which imparted motility to such coacervates in presence of substrate. This mechanism was confirmed by stochastic modelling and simulations in silico. Furthermore, we showed that a deeper understanding of the mechanism of stochasticity could be utilized to modulate the motion output. Conceptually, this work represents a leap in design philosophy in the construction of synthetic systems with life-like behaviors.

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Author Correction: Engineering transient dynamics of artificial cells by stochastic distribution of enzymes

Nature Communications ◽

10.1038/s41467-021-27682-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Shidong Song ◽

Alexander F. Mason ◽

Richard A. J. Post ◽

Marco De Corato ◽

Rafael Mestre ◽

...

Keyword(s):

Transient Dynamics ◽

Artificial Cells ◽

Stochastic Distribution

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Engineering transient dynamics of artificial cells by stochastic distribution of enzymes

10.33774/chemrxiv-2021-02c1d ◽

2021 ◽

Author(s):

Jan C. van Hest ◽

Shidong Song ◽

Alexander Mason ◽

Richard Post ◽

Marco de Corato ◽

...

Keyword(s):

Transient Dynamics ◽

Artificial Cells ◽

Stochastic Distribution

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Nonlinear stochastic modelling with Langevin regression

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2021.0092 ◽

2021 ◽

Vol 477 (2250) ◽

pp. 20210092

Author(s):

J. L. Callaham ◽

J.-C. Loiseau ◽

G. Rigas ◽

S. L. Brunton

Keyword(s):

Langevin Equation ◽

Degrees Of Freedom ◽

Bluff Body ◽

Gaussian White Noise ◽

Stochastic Modelling ◽

State Dependent ◽

Nonlinear Langevin Equation ◽

Statistical Consistency ◽

Fokker Planck Equations ◽

Random Fluctuations

Many physical systems characterized by nonlinear multiscale interactions can be modelled by treating unresolved degrees of freedom as random fluctuations. However, even when the microscopic governing equations and qualitative macroscopic behaviour are known, it is often difficult to derive a stochastic model that is consistent with observations. This is especially true for systems such as turbulence where the perturbations do not behave like Gaussian white noise, introducing non-Markovian behaviour to the dynamics. We address these challenges with a framework for identifying interpretable stochastic nonlinear dynamics from experimental data, using forward and adjoint Fokker–Planck equations to enforce statistical consistency. If the form of the Langevin equation is unknown, a simple sparsifying procedure can provide an appropriate functional form. We demonstrate that this method can learn stochastic models in two artificial examples: recovering a nonlinear Langevin equation forced by coloured noise and approximating the second-order dynamics of a particle in a double-well potential with the corresponding first-order bifurcation normal form. Finally, we apply Langevin regression to experimental measurements of a turbulent bluff body wake and show that the statistical behaviour of the centre of pressure can be described by the dynamics of the corresponding laminar flow driven by nonlinear state-dependent noise.

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A combined within-host and between-hosts modelling framework for the evolution of resistance to antimalarial drugs

Journal of The Royal Society Interface ◽

10.1098/rsif.2016.0148 ◽

2016 ◽

Vol 13 (117) ◽

pp. 20160148 ◽

Cited By ~ 26

Author(s):

Mathieu Legros ◽

Sebastian Bonhoeffer

Keyword(s):

Disease Incidence ◽

Stochastic Modelling ◽

Human Infection ◽

Infection Model ◽

Significant Challenge ◽

Modelling Framework ◽

Infection Dynamics ◽

Complex Process ◽

Evolution Of Resistance ◽

Primary Focus

The spread of drug resistance represents a significant challenge to many disease control efforts. The evolution of resistance is a complex process influenced by transmission dynamics between hosts as well as infection dynamics within these hosts. This study aims to investigate how these two processes combine to impact the evolution of resistance in malaria parasites. We introduce a stochastic modelling framework combining an epidemiological model of Plasmodium transmission and an explicit within-human infection model for two competing strains. Immunity, treatment and resistance costs are included in the within-host model. We show that the spread of resistance is generally less likely in areas of intense transmission, and therefore of increased competition between strains, an effect exacerbated when costs of resistance are higher. We also illustrate how treatment influences the spread of resistance, with a trade-off between slowing resistance and curbing disease incidence. We show that treatment coverage has a stronger impact on disease prevalence, whereas treatment efficacy primarily affects resistance spread, suggesting that coverage should constitute the primary focus of control efforts. Finally, we illustrate the importance of feedbacks between modelling scales. Overall, our results underline the importance of concomitantly modelling the evolution of resistance within and between hosts.

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Study of the transient dynamics of coarse-grained molecular systems with the path-space force-matching method

Procedia Computer Science ◽

10.1016/j.procs.2019.08.180 ◽

2019 ◽

Vol 156 ◽

pp. 59-68

Author(s):

Georgia Baxevani ◽

Evangelia Kalligiannaki ◽

Vagelis Harmandaris

Keyword(s):

Path Space ◽

Coarse Grained ◽

Transient Dynamics ◽

Matching Method ◽

Molecular Systems ◽

Force Matching

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A crystalline–amorphous Ni–Ni(OH)2 core–shell catalyst for the alkaline hydrogen evolution reaction

Journal of Materials Chemistry A ◽

10.1039/d0ta08735a ◽

2020 ◽

Vol 8 (44) ◽

pp. 23323-23329

Author(s):

Jing Hu ◽

Siwei Li ◽

Yuzhi Li ◽

Jing Wang ◽

Yunchen Du ◽

...

Keyword(s):

Hydrogen Evolution ◽

Hydrogen Evolution Reaction ◽

Electrocatalytic Activity ◽

Core Shell ◽

Alkaline Conditions

Crystalline–amorphous Ni–Ni(OH)2 core–shell assembled nanosheets exhibit outstanding electrocatalytic activity and stability for hydrogen evolution under alkaline conditions.

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Artificial cells containing sustainable energy conversion engines

Emerging Topics in Life Sciences ◽

10.1042/etls20190103 ◽

2019 ◽

Vol 3 (5) ◽

pp. 573-578 ◽

Cited By ~ 3

Author(s):

Kwanwoo Shin

Keyword(s):

Energy Conversion ◽

Nicotinamide Adenine Dinucleotide ◽

Sustainable Energy ◽

Living Cells ◽

Energy Transduction ◽

Artificial Cells ◽

Energy Conversion Process ◽

Metabolic Reactions ◽

Metabolic Activities ◽

Reduced Nicotinamide Adenine Dinucleotide

Living cells naturally maintain a variety of metabolic reactions via energy conversion mechanisms that are coupled to proton transfer across cell membranes, thereby producing energy-rich compounds. Until now, researchers have been unable to maintain continuous biochemical reactions in artificially engineered cells, mainly due to the lack of mechanisms that generate energy-rich resources, such as adenosine triphosphate (ATP) and reduced nicotinamide adenine dinucleotide (NADH). If these metabolic activities in artificial cells are to be sustained, reliable energy transduction strategies must be realized. In this perspective, this article discusses the development of an artificially engineered cell containing a sustainable energy conversion process.

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