Chaos in synthetic microbial communities

Predictability is a fundamental requirement in biological engineering. As we move to building coordinated multicellular systems, the potential for such systems to display chaotic behaviour becomes a concern. Therefore understanding which systems show chaos is an important design consideration. We developed a methodology to explore the potential for chaotic dynamics in small microbial communities governed by resource competition, intercellular communication and competitive bacteriocin interactions. Our model selection pipeline uses Approximate Bayesian Computation to first identify oscillatory behaviours as a route to finding chaotic behaviour. We have shown that we can expect to find chaotic states in relatively small synthetic microbial systems, understand the governing dynamics and provide insights into how to control such systems. This work is the first to query the existence of chaotic behaviour in synthetic microbial communities and has important ramifications for the fields of biotechnology, bioprocessing and synthetic biology

GPU accelerated biochemical network simulation

Motivation: Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code

CNETML: maximum likelihood inference of phylogeny from copy number profiles of multiple samples

Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation

GPU accelerated biochemical network simulation

Motivation: Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and outperform CPUs in terms of raw computing power. Despite their computational advantages, their adoption by the systems biology community is relatively slow, since differences in hardware architecture between GPUs and CPUs complicate the porting of existing code

Model selection in systems biology depends on experimental design.

Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis

Tier-specific evolution of match performance characteristics in the English Premier League: it's getting tougher at the top.

This study investigated the evolution of physical and technical performances in the English Premier League (EPL), with special reference to league ranking. Match performance observations (n = 14,700) were collected using a multiple-camera computerised tracking system across seven consecutive EPL seasons (2006-07 to 2012-13). Final league rankings were classified into Tiers: (A) 1st-4th ranking (n = 2519), (B) 5th-8th ranking (n = 2965), (C) 9th-14th ranking (n = 4448) and (D) 15th-20th ranking (n = 4768). Teams in Tier B demonstrated moderate increases in high-intensity running distance while in ball possession from the 2006-07 to 2012-13 season (P < 0.001; effect size [ES]: 0.68), with Tiers A, C and D producing less pronounced increases across the same period (P < 0.005; ES: 0.26, 0.41 and 0.33, respectively). Large increases in sprint distance were observed from the 2006-07 to 2012-13 season for Tier B (P < 0.001; ES: 1.21), while only moderate increases were evident for Tiers A, C and D (P < 0.001; ES: 0.75, 0.97 and 0.84, respectively). Tier B demonstrated large increases in the number of passes performed and received in 2012-13 compared to 2006-07 (P < 0.001; ES: 1.32-1.53) with small-to-moderate increases in Tier A (P < 0.001; ES: 0.30-0.38), Tier C (P < 0.001; ES: 0.46-0.54) and Tier D (P < 0.001; ES: 0.69-0.87). The demarcation line between 4th (bottom of Tier A) and 5th ranking (top of Tier B) in the 2006-07 season was 8 points, but this decreased to just a single point in the 2012-13 season. The data demonstrate that physical and technical performances have evolved more in Tier B than any other Tier in the EPL and could indicate a narrowing of the performance gap between the top two Tiers