Genetic algorithm-based control of birefringent filtering for self-tuning, self-pulsing fiber lasers

Polarization-based filtering in fiber lasers is well-known to enable spectral tunability and a wide range of dynamical operating states. This effect is rarely exploited in practical systems, however, because optimization of cavity parameters is non-trivial and evolves due to environmental sensitivity. Here, we report a genetic algorithm-based approach, utilizing electronic control of the cavity transfer function, to autonomously achieve broad wavelength tuning and the generation of Q-switched pulses with variable repetition rate and duration. The practicalities and limitations of simultaneous spectral and temporal self-tuning from a simple fiber laser are discussed, paving the way to on-demand laser properties through algorithmic control and machine learning schemes.Comment: Accepted for Optics Letters, 12th June 201

Combined burden and functional impact tests for cancer driver discovery using DriverPower

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery

Auto-tuning for high performance autopilot design

A novel auto-tuning method for the RIDE controller algorithm is presented. The RIDE controller is applied to a high performance aircraft model. The tuner utilises a constrained genetic algorithm to automate the tuning process. The results of the tuner are compared with that of another tuning method which utilises unconstrained optimisation so as to highlight the efficacy of constrained optimisation for this application. It is shown from the results that the constrained genetic algorithm optimisation scheme offers a highly effective tuning solution which can be used to attain safe and high performance control with the RIDE control algorithm

Towards engineering microbial consortia using RNA-based genetic controllers

In nature, quorum sensing is a mechanism used by microbes to communicate and coordinate behaviours at the population level. Over the last two decades, synthetic biologists have used the unique property of quorum sensing to sense population density for coordinating cellular behaviours of single and mixed cultures. This gave rise to the development of multicellular biosynthesis systems for metabolic engineering and of spatially distributed systems for synthetic biology. However, robustly controlling the composition of multicellular systems remains a challenge and limits its wide adoption by the metabolic engineering community. Current strategies for controlling synthetic microbial communities vastly rely on engineer- ing metabolic dependencies between microbial species in a process called syntrophy. While syntrophy guarantees the survival of all strains in the coculture, it does not provide a way to control community composition. Existing genetic circuits that can dynamically control community composition often impose too much burden on their hosts for division of labour to be a viable solution to improve yields and titers of valuable metabolic products. Here we investigate the potential of using RNA-based gene circuits to reduce the cost of express- ing heterologous genes for the control of community composition in a two-member E. coli coculture. In this work, we present the development of three genetic modules that rely on RNA species to detect changes in population density and to regulate growth rate when community composition becomes unstable. Together, the modules work in concert to stabilise community composition around a ratio set by the intrinsic properties of the circuit’s genetic components. We identify the key parameters of the circuits that enable tuning of the composition ratio. We characterise the cost of expressing each module of the genetic controller by measuring its impact on the host growth rate and on consumption of free cellular resources. Together these findings highlight the importance of developing host-aware circuits to control community composition so as to enable their wide adoption by metabolic engineers.Open Acces

Hyper-parameter tuning for the (1+ (λ, λ)) GA

It is known that the (1 + (λ, λ)) Genetic Algorithm (GA) with self-adjusting parameter choices achieves a linear expected optimization time on OneMax if its hyper-parameters are suitably chosen. However, it is not very well understood how the hyper-parameter settings influences the overall performance of the (1 + (λ, λ)) GA. Analyzing such multi-dimensional dependencies precisely is at the edge of what running time analysis can offer. To make a step forward on this question, we present an in-depth empirical study of the self-adjusting (1 + (λ, λ)) GA and its hyper-parameters. We show, among many other results, that a 15% reduction of the average running time is possible by a slightly different setup, which allows non-identical offspring population sizes of mutation and crossover phase, and more flexibility in the choice of mutation rate and crossover bias --- a generalization which may be of independent interest. We also show indication that the parametrization of mutation rate and crossover bias derived by theoretical means for the static variant of the (1 + (λ, λ)) GA extends to the non-static case.Postprin

Towards 'smart lasers': self-optimisation of an ultrafast pulse source using a genetic algorithm

Short-pulse fibre lasers are a complex dynamical system possessing a broad space of operating states that can be accessed through control of cavity parameters. Determination of target regimes is a multi-parameter global optimisation problem. Here, we report the implementation of a genetic algorithm to intelligently locate optimum parameters for stable single-pulse mode-locking in a Figure-8 fibre laser, and fully automate the system turn-on procedure. Stable ultrashort pulses are repeatably achieved by employing a compound fitness function that monitors both temporal and spectral output properties of the laser. Our method of encoding photonics expertise into an algorithm and applying machine-learning principles paves the way to self-optimising `smart' optical technologies

GA tuning of pitch controller for small scale MAVs

The paper presents the application of intelligent tuning methods for the control of a prototype MAV in order to address problems associated with bandwidth limited actuators and gust alleviation. Specifically, as a proof of concept, the investigation is focused on the pitch control of a MAV. The work is supported by experimental results from wind tunnel testing that shows the merits of the use of Genetic Algorithm (GA) tuning techniques compared to classical, empirical tuning methodologies. To provide a measure of relative merit, the controller responses are evaluated using the ITAE performance index. In this way, the proposed method is shown to induce far superior dynamic performance compared to traditional approaches

Synthetic chemical inducers and genetic decoupling enable orthogonal control of the rhaBAD promoter

External control of gene expression is crucial in synthetic biology and biotechnology research and applications, and is commonly achieved using inducible promoter systems. The E. coli rhamnose-inducible rhaBAD promoter has properties superior to more commonly-used inducible expression systems, but is marred by transient expression caused by degradation of the native inducer, L-rhamnose. To address this problem, 35 analogs of L-rhamnose were screened for induction of the rhaBAD promoter, but no strong inducers were identified. In the native configuration, an inducer must bind and activate two transcriptional activators, RhaR and RhaS. Therefore, the expression system was reconfigured to decouple the rhaBAD promoter from the native rhaSR regulatory cascade so that candidate inducers need only activate the terminal transcription factor RhaS. Re-screening the 35 compounds using the modified rhaBAD expression system revealed several promising inducers. These were characterised further to determine the strength, kinetics and concentration-dependence of induction; whether the inducer was used as a carbon source by E. coli; and the modality (distribution) of induction among populations of cells. L-Mannose was found to be the most useful orthogonal inducer, providing an even greater range of induction than the native inducer Lrhamnose, and crucially, allowing sustained induction instead of transient induction. These findings address the key limitation of the rhaBAD expression system, and suggest it may now be the most suitable system for many applications

Non-convex Global Minimization and False Discovery Rate Control for the TREX

The TREX is a recently introduced method for performing sparse high-dimensional regression. Despite its statistical promise as an alternative to the lasso, square-root lasso, and scaled lasso, the TREX is computationally challenging in that it requires solving a non-convex optimization problem. This paper shows a remarkable result: despite the non-convexity of the TREX problem, there exists a polynomial-time algorithm that is guaranteed to find the global minimum. This result adds the TREX to a very short list of non-convex optimization problems that can be globally optimized (principal components analysis being a famous example). After deriving and developing this new approach, we demonstrate that (i) the ability of the preexisting TREX heuristic to reach the global minimum is strongly dependent on the difficulty of the underlying statistical problem, (ii) the new polynomial-time algorithm for TREX permits a novel variable ranking and selection scheme, (iii) this scheme can be incorporated into a rule that controls the false discovery rate (FDR) of included features in the model. To achieve this last aim, we provide an extension of the results of Barber & Candes (2015) to establish that the knockoff filter framework can be applied to the TREX. This investigation thus provides both a rare case study of a heuristic for non-convex optimization and a novel way of exploiting non-convexity for statistical inference