Control of stochastic and induced switching in biophysical networks

Noise caused by fluctuations at the molecular level is a fundamental part of intracellular processes. While the response of biological systems to noise has been studied extensively, there has been limited understanding of how to exploit it to induce a desired cell state. Here we present a scalable, quantitative method based on the Freidlin-Wentzell action to predict and control noise-induced switching between different states in genetic networks that, conveniently, can also control transitions between stable states in the absence of noise. We apply this methodology to models of cell differentiation and show how predicted manipulations of tunable factors can induce lineage changes, and further utilize it to identify new candidate strategies for cancer therapy in a cell death pathway model. This framework offers a systems approach to identifying the key factors for rationally manipulating biophysical dynamics, and should also find use in controlling other classes of noisy complex networks.Comment: A ready-to-use code package implementing the method described here is available from the authors upon reques

Development of a MATLAB/Simulink - Arduino environment for experimental practices in control engineering teaching

This project presents the steps followed when implementing a platform based on MATLAB/Simulink and Arduino for the restoration of digital control practices. During this project, an Arduino shield has being designed. Along with this, a web page has also been created where all the material done during all this project is available and can be freely used. So anyone interested on doing a project can have a starting point instead of starting a project from scratch, which most of times this results hard to implement. Taking all this into account, the document is structured in the following manner. The first chapter talks about the hardware used and designed. The second one explains the software used and the configurations done on the laboratory’s PCs. After that, the web page Duino-Based Learning is explained, where you can find the five projects carried out in the "Control Automàtic" subject with their corresponding results. In this section too, as an additional research, the implemented indirect adaptive control will be explained, where the parameter estimation has been done by the Recursive Least Square algorithm. The last four sections before presenting the conclusions of the work, correspond to a satisfaction questionnaire done to the teachers that have used the setup, the costs and saves of the project, the environmental impact and the planning of the project respectively

A New Data Source for Inverse Dynamics Learning

Modern robotics is gravitating toward increasingly collaborative human robot interaction. Tools such as acceleration policies can naturally support the realization of reactive, adaptive, and compliant robots. These tools require us to model the system dynamics accurately -- a difficult task. The fundamental problem remains that simulation and reality diverge--we do not know how to accurately change a robot's state. Thus, recent research on improving inverse dynamics models has been focused on making use of machine learning techniques. Traditional learning techniques train on the actual realized accelerations, instead of the policy's desired accelerations, which is an indirect data source. Here we show how an additional training signal -- measured at the desired accelerations -- can be derived from a feedback control signal. This effectively creates a second data source for learning inverse dynamics models. Furthermore, we show how both the traditional and this new data source, can be used to train task-specific models of the inverse dynamics, when used independently or combined. We analyze the use of both data sources in simulation and demonstrate its effectiveness on a real-world robotic platform. We show that our system incrementally improves the learned inverse dynamics model, and when using both data sources combined converges more consistently and faster.Comment: IROS 201

Adaptive Predictive Control Using Neural Network for a Class of Pure-feedback Systems in Discrete-time

10.1109/TNN.2008.2000446IEEE Transactions on Neural Networks1991599-1614ITNN