On the Exploitation of Search History and Accumulative Sampling in Robust Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.Efficient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution’s uncertainty neighbourhood. We propose a full exploitation of the search history by updating the robust fitness approximations across the entire search history rather than a fixed population. Our proposed method shows promising results on a range of test problems compared with other approaches from the literature.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]

Optimisation and Landscape Analysis of Computational Biology Models: A Case Study

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.The parameter explosion problem is a crucial bottleneck in modelling gene regulatory networks (GRNs), limiting the size of models that can be optimised to experimental data. By discretising state, but not time, Boolean delay equations (BDEs) provide a signi ficant reduction in parameter numbers, whilst still providing dynamical complexity comparable to more biochemically detailed models, such as those based on differential equations. Here, we explore several approaches to optimising BDEs to timeseries data, using a simple circadian clock model as a case study. We compare the ffectiveness of two optimisers on our problem: a genetic algorithmf(GA) and an elite accumulative sampling (EAS) algorithm that provides robustness to data discretisation. Our results show that both methods are able to distinguish effectively between alternative architectures, yielding excellent ts to data. We also perform a landscape analysis, providing insights into the properties that determine optimiser performance (e.g. number of local optima and basin sizes). Our results provide a promising platform for the analysis of more complex GRNs, and suggest the possibility of leveraging cost landscapes to devise more effi cient optimisation schemes.This work was financially supported by the Engineering and Physical Sciences Research Council [grant numbers EP/N017846/1, EP/N014391/1], and made use of the Zeus and Isca supercomputing facilities provided by the University of Exeter HPC Strategy

Robust Optimisation using Voronoi-Based Archive Sampling

Engineering and Physical Sciences Research Council (EPSRC

Robust Multi-Modal Optimisation

Robust and multi-modal optimisation are two important topics that have received significant attention from the evolutionary computation community over the past few years. However, the two topics have usually been investigated independently and there is a lack of work that explores the important intersection between them. This is because there are real-world problems where both formulations are appropriate in combination. For instance, multiple ‘good’ solutions may be sought which are distinct in design space for an engineering problem – where error between the computational model queried during optimisation and the real engineering environment is believed to exist (a common justification for multi-modal optimisation) – but also engineering tolerances may mean a realised design might not exactly match the inputted specification (a robust optimisation problem). This paper conducts a preliminary examination of such intersections and identifies issues that need to be addressed for further advancement in this new area. The paper presents initial benchmark problems and examines the performance of combined state-of-the-art methods from both fields on these problems.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]

Landscape Analysis Under Measurement Error

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordThere are situations where the need for optimisation with a global precision tolerance arises — for example, due to measurement, numerical or evaluation errors in the objective function. In such situations, a global tolerance ε > 0 can be predefined such that two objective values are declared equal if the absolute difference between them is less than or equal to ε. This paper presents an overview of fitness landscape analysis under such conditions. We describe the formulation of common landscape categories in the presence of a global precision tolerance. We then proceed by dis- cussing issues that can emerge as a result of using tolerance, such as the increase in the neutrality of the fitness landscape. To this end, we propose two methods to exhaustively explore plateaus in such application domains — one of which is point-based and the other of which is set-based.Engineering and Physical Sciences Research Council (EPSRC

Voronoi-Based Archive Sampling for Robust Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordWe propose a framework for estimating the quality of solutions in a robust optimisation setting by utilising samples from the search history and using MC sampling to approximate a Voronoi tessellation. This is used to determine a new point in the disturbance neighbourhood of a given solution such that – along with the relevant archived points – they form a well-spread distribution, and is also used to weight the archive points to mitigate any selection bias in the neighbourhood history. Our method performs comparably well with existing frameworks when implemented inside a CMA-ES on 9 test problems collected from the literature in 2 and 10 dimensions.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1]

Light and circadian regulation of clock components aids flexible responses to environmental signals

The circadian clock measures time across a 24h period, increasing fitness by phasing biological processes to the most appropriate time of day. The interlocking feedback loop mechanism of the clock is conserved across species; however, the number of loops varies. Mathematical and computational analyses have suggested that loop complexity affects the overall flexibility of the oscillator, including its responses to entrainment signals. We used a discriminating experimental assay, at the transition between different photoperiods, in order to test this proposal in a minimal circadian network (in Ostreococcus tauri) and a more complex network (in Arabidopsis thaliana). Transcriptional and translational reporters in O.tauri primarily tracked dawn or dusk, whereas in A.thaliana, a wider range of responses were observed, consistent with its more flexible clock. Model analysis supported the requirement for this diversity of responses among the components of the more complex network. However, these and earlier data showed that the O.tauri network retains surprising flexibility, despite its simple circuit. We found that models constructed from experimental data can show flexibility either from multiple loops and/or from multiple light inputs. Our results suggest that O.tauri has adopted the latter strategy, possibly as a consequence of genomic reduction

The Input Signal Step Function (ISSF), a Standard Method to Encode Input Signals in SBML Models with Software Support, Applied to Circadian Clock Models

LetterThis is the final version of the article. Available from SAGE Publications via the DOI in this record.Time-dependent light input is an important feature of computational models of the circadian clock. However, publicly available models encoded in standard representations such as the Systems Biology Markup Language (SBML) either do not encode this input or use different mechanisms to do so, which hinders reproducibility of published results as well as model reuse. The authors describe here a numerically continuous function suitable for use in SBML for models of circadian rhythms forced by periodic light-dark cycles. The Input Signal Step Function (ISSF) is broadly applicable to encoding experimental manipulations, such as drug treatments, temperature changes, or inducible transgene expression, which may be transient, periodic, or mixed. It is highly configurable and is able to reproduce a wide range of waveforms. The authors have implemented this function in SBML and demonstrated its ability to modify the behavior of publicly available models to accurately reproduce published results. The implementation of ISSF allows standard simulation software to reproduce specialized circadian protocols, such as the phase-response curve. To facilitate the reuse of this function in public models, the authors have developed software to configure its behavior without any specialist knowledge of SBML. A community-standard approach to represent the inputs that entrain circadian clock models could particularly facilitate research in chronobiology.K.S. was supported by the UK BBSRC grant BB/E015263/1. SynthSys Edinburgh is a Centre for Integrative Systems Biology (CISB) funded by BBSRC and EPSRC, reference BB/D019621/1

Genome-wide quantitative analysis of DNA methylation from bisulfite sequencing data

Summary: Here we present the open-source R/Bioconductor software package BEAT (BS-Seq Epimutation Analysis Toolkit). It implements all bioinformatics steps required for the quantitative high-resolution analysis of DNA methylation patterns from bisulfite sequencing data, including the detection of regional epimutation events, i.e. loss or gain of DNA methylation at CG positions relative to a reference. Using a binomial mixture model, the BEAT package aggregates methylation counts per genomic position, thereby compensating for low coverage, incomplete conversion and sequencing errors. Availability and implementation: BEAT is freely available as part of Bioconductor at www.bioconductor.org/packages/devel/bioc/html/BEAT.html. The package is distributed under the GNU Lesser General Public License 3.0. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online