Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.

Generational techniques have been very successful in reducing the impact of garbage collection algorithms upon the performance of programs. However, all generational algorithms occasionally promote objects that later become garbage, resulting in an accumulation of garbage in older generations. Reclaiming this tenured garbage without resorting to collecting the entire heap is a difficult problem. In this paper, we describe a mechanism that extends existing generational collection algorithms by allowing them to reclaim tenured garbage more effectively. In particular, our dynamic threatening boundary mechanism divides memory into two spaces, one for shortlived, and another for long-lived objects. Unlike previous work, our collection mechanism can dynamically adjust the boundary between these two spaces either forward or backward in time, essentially allowing data to become untenured. We describe an implementation of the dynamic threatening boundary mechanism and quantify its associated costs. We also describe a policy for setting the threatening boundary and evaluate its performance relative to existing generational collection algorithms. Our results show that a policy that uses the dynamic threatening boundary mechanism is effective at reclaiming tenured garbage.

Dynamic storage allocation is used heavily in many application areas including interpreters, simulators, optimizers, and translators. We describe research that can improve all aspects of the performance of dynamic storage allocation by predicting the lifetimes of short-lived objects when they are allocated. Using five significant, allocation-intensive C programs, we show that a great fraction of all bytes allocated are short-lived (> 90% in all cases). Furthermore, we describe an algorithm for liftetime prediction that accurately predicts the lifetimes of 42–99% of all objects allocated. We describe and simulate a storage allocator that takes adavantage of lifetime prediction of short-lived objects and show that it can significantly improve a program's memory overhead and reference locality, and even, at times, improve CPU performance as well.