748,844 research outputs found
Optimal control of transitions between nonequilibrium steady states
Biological systems fundamentally exist out of equilibrium in order to
preserve organized structures and processes. Many changing cellular conditions
can be represented as transitions between nonequilibrium steady states, and
organisms have an interest in optimizing such transitions. Using the
Hatano-Sasa Y-value, we extend a recently developed geometrical framework for
determining optimal protocols so that it can be applied to systems driven from
nonequilibrium steady states. We calculate and numerically verify optimal
protocols for a colloidal particle dragged through solution by a translating
optical trap with two controllable parameters. We offer experimental
predictions, specifically that optimal protocols are significantly less costly
than naive ones. Optimal protocols similar to these may ultimately point to
design principles for biological energy transduction systems and guide the
design of artificial molecular machines.Comment: Accepted for publication at PLoS ON
Internal agent states : experiments using the swarm leader concept
In recent years, an understanding of the operating principles and stability of natural swarms has proven to be a useful tool for the design and control of artificial robotic agents. Many robotic systems, whose design or control principals are inspired by behavioural aspects of real biological systems such as leader-follower relationship, have been developed. We introduced an algorithm which successfully enhances the navigation performance of a swarm of robots using the swarm leader concept. This paper presents some applications based on that work using the simulations and experimental implementation using a swarming behaviour test-bed at the University of Strathclyde. Experimental and simulation results match closely in a way that confirms the efficiency of the algorithm as well as its applicability
Measurement set selection of parameter estimation in biological system modelling - a case study of signal transduction pathways
Parameter estimation is a challenging problem for biological systems modelling since the model is normally of high dimension, the measurement data are sparse and noisy and the cost of experiments high. Accurate recovery of parameters depends on the quality and quantity of measurement data. It is therefore important to know which measurements to be taken when and how through optimal experimental design (OED). In this paper a method was proposed to determine the most informative measurement set for parameter estimation of dynamic systems, in particular biochemical reaction systems, such that the unknown parameters can be inferred with the best possible statistical quality using the data collected from the designed experiments. System analysis using matrix theory was used to examine the number of necessary measurement variables. The priority of each measurement variable was determined by optimal experimental design based on Fisher information matrix (FIM). The applicability and advantages of the proposed method were shown through an example of signal pathway model
Determine measurement set for parameter estimation in biological systems modeling
Parameter estimation is challenging for biological systems modelling since the model is normally of high dimension, the measurement data are sparse and noisy, and the cost of experiments is high. Accurate recovery of parameters depend on the quantity and quality of measurement data. It is therefore important to know what measurements to be taken, when and how through optimal experimental design (OED). In this paper we present a method to determine the most informative measurement set for parameter estimation of dynamic systems, in particular biochemical reaction systems, such that the unknown parameters can be inferred with the best possible statistical quality using the data collected from the designed experiments. System analysis using matrix theory is introduced to examine the number of necessary measurement variables. The priority of each measurement variable is determined by optimal experimental design based on Fisher information matrix (FIM). The applicability and advantages of the proposed method are illustrated through an example of a signal pathway model
A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints
Accurate estimation of parameters is paramount in developing high-fidelity
models for complex dynamical systems. Model-based optimal experiment design
(OED) approaches enable systematic design of dynamic experiments to generate
input-output data sets with high information content for parameter estimation.
Standard OED approaches however face two challenges: (i) experiment design
under incomplete system information due to unknown true parameters, which
usually requires many iterations of OED; (ii) incapability of systematically
accounting for the inherent uncertainties of complex systems, which can lead to
diminished effectiveness of the designed optimal excitation signal as well as
violation of system constraints. This paper presents a robust OED approach for
nonlinear systems with arbitrarily-shaped time-invariant probabilistic
uncertainties. Polynomial chaos is used for efficient uncertainty propagation.
The distinct feature of the robust OED approach is the inclusion of chance
constraints to ensure constraint satisfaction in a stochastic setting. The
presented approach is demonstrated by optimal experimental design for the
JAK-STAT5 signaling pathway that regulates various cellular processes in a
biological cell.Comment: Submitted to ADCHEM 201
TESTING USED ROLLER BEARINGS FOR QUALITY AND SERVICE LIFE
Synthetic biology is a rapidly expanding field at the interface of the engineering and biological sciences which aims to apply rational design principles in biological contexts. Many natural processes utilise regulatory architectures that parallel those found in control and electrical engineering, which has motivated their implementation as part of synthetic biological constructs. Tools based upon control theoretical concepts can be used to design such systems, as well as to guide their experimental realisation. In this paper we provide examples of biological implementations of negative feedback systems, and discuss progress made toward realisation of other feedback and control architectures. We then outline major challenges posed by the design of such systems, particularly focusing on those which are specific to biological contexts and on which feedback control can have a significant impact. We explore future directions for work in the field, including new approaches for theoretical design of biological control systems, the utilisation of novel components for their implementation, and the potential for application of automation and machine-learning approaches to accelerate synthetic biological research
Numerical coupling of fluid and structure in cardiac flow and devices
Numerical simulations are a powerful tool in investigation of flow and structure
dynamics in biological systems and in the design of biomedical devices. Time-dependent
fluid-structure interaction (FSI) problems in biological systems are often characterized by
a periodic nature and relatively low Reynolds number. In order to solve the dynamics of
the fluid and structure of coupled systems, different approaches may be used. Several
parameters such as geometrical complexity, degree of displacement, convergence to
steady periodicity, and the system stability may determine the coupling method. In the
talk, four numerical studies of biological and implanted systems will be presented, each
with a different FSI approach. The first study is of flow through mechanical heart valves,
using finite-volume (FV) fluid solver coupled with an external structural solver using a
weak coupling scheme for large displacements. The second study is of flow inside a
pulsatile ventricular assist device with FV fluid solver coupled with finite-element (FE)
structure solver using a strong staggered coupling assuming small displacements. The
third study is of flow through vulnerable plaque in the coronary arteries, with FE solvers
for both the fluid and structure domains, using a fully-coupled iterative scheme assuming
small displacements. The fourth simulation is of an impedance pump using a direct FE
coupling method for large displacements. In addition to the methodology, the applicative
design and hemodynamic aspects of the cases will be discussed, including washout
properties and risk for thrombosis. The results obtained from the studies will be
compared to experimental analyses
Model-guided design of ligand-regulated RNAi for programmable control of gene expression
Progress in constructing biological networks will rely on the development of more advanced components that can be predictably modified to yield optimal system performance. We have engineered an RNA-based platform, which we call an shRNA switch, that provides for integrated ligand control of RNA interference (RNAi) by modular coupling of an aptamer, competing strand, and small hairpin (sh) RNA stem into a single component that links ligand concentration and target gene expression levels. A combined experimental and mathematical modelling approach identified multiple tuning strategies and moves towards a predictable framework for the forward design of shRNA switches. The utility of our platform is highlighted by the demonstration of fine-tuning, multi-input control, and model-guided design of shRNA switches with an optimized dynamic range. Thus, shRNA switches can serve as an advanced component for the construction of complex biological systems and offer a controlled means of activating RNAi in disease therapeutics
- …