Engineering transient dynamics of artificial cells by stochastic distribution of enzymes

Random 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.

Quantitative Detection of Disseminated Melanoma Cells by Trp-1 Transcript Analysis Reveals Stochastic Distribution of Pulmonary Metastases

A better understanding of the process of melanoma metastasis is required to underpin the development of novel therapies that will improve patient outcomes. The use of appropriate animal models is indispensable for investigating the mechanisms of melanoma metastasis. However, reliable and practicable quantification of metastases in experimental mice remains a challenge, particularly if the metastatic burden is low. Here, we describe a qRT-PCR-based protocol that employs the melanocytic marker Trp-1 for the sensitive quantification of melanoma metastases in the murine lung. Using this protocol, we were able to detect the presence of as few as 100 disseminated melanoma cells in lung tissue. This allowed us to quantify metastatic burden in a spontaneous syngeneic B16-F10 metastasis model, even in the absence of visible metastases, as well as in the autochthonous Tg(Grm1)/Cyld−/− melanoma model. Importantly, we also observed an uneven distribution of disseminated melanoma cells amongst the five lobes of the murine lung, which varied considerably from animal to animal. Together, our findings demonstrate that the qRT-PCR-based detection of Trp-1 allows the quantification of low pulmonary metastatic burden in both transplantable and autochthonous murine melanoma models, and show that the analysis of lung metastasis in such models needs to take into account the stochastic distribution of metastatic lesions amongst the lung lobes.

Stochastic distribution tracking control for stochastic non-linear systems via probability density function vectorisation

This paper presents a new control strategy for stochastic distribution shape tracking regarding non-Gaussian stochastic non-linear systems. The objective can be summarised as adjusting the probability density function (PDF) of the system output to any given desired distribution. In order to achieve this objective, the system output PDF has first been formulated analytically, which is time-variant. Then, the PDF vectorisation has been implemented to simplify the model description. Using the vector-based representation, the system identification and control design have been performed to achieve the PDF tracking. In practice, the PDF evolution is difficult to implement in real-time, thus a data-driven extension has also been discussed in this paper, where the vector-based model can be obtained using kernel density estimation (KDE) with the real-time data. Furthermore, the stability of the presented control design has been analysed, which is validated by a numerical example. As an extension, the multi-output stochastic systems have also been discussed for joint PDF tracking using the proposed algorithm, and the perspectives of advanced controller have been discussed. The main contribution of this paper is to propose: (1) a new sampling-based PDF transformation to reduce the modelling complexity, (2) a data-driven approach for online implementation without model pre-training, and (3) a feasible framework to integrate the existing control methods.

Stochastic Distribution Controller for Wind Turbines with Doubly Fed Induction Generator

The major purpose of this work is to design the controllers for controlling the variable speed, variable pitch wind turbine (WT) with doubly fed induction generator (DFIG). Vector control strategy is adopted for controlling the DFIG active and reactive power. Generator torque is control to provide the regulated real power with minimum fluctuation. The fixed gain proportional-integral (PI) controller designed to the converter of rotor side and grid side has limited operating range and inherent overshoot. Gain scheduling PI controller is designed to minimize the overshoot and fluctuation exists in proportional-integral controller. Since DFIG based wind energy conversion system (WECS) works in uncertain wind speed, stochastic distribution control (SDC) method is proposed to control the probability distribution function (PDF) of DFIG based WECS. It copes with nonlinearities in the WECS and contiguous variations at operating point and provides satisfactory performance for the whole operating region. It improves the performance together with power quality of generated electric power thereby maximizing the lifespan of installation and ensures secure and acceptable operation of the DFIG based WECS.