Improving SSE parallel code with grow and graft genetic programming

RNAfold predicts the secondary structure of RNA molecules from their base sequence. We apply a mixture of manual and automated genetic improvements to its C source. GI gives a 1.6% improvement to parallel SSE4.1 code. The automatic programming evolutionary system has access to Intel library code and previous revisions. On 4 666 curated structures from RNA STRAND, GGGP gives a combined speed up of 31.9%, with no loss of accuracy (GI code run 1:4 1011 times)

Genetic Improvement of computational biology software

There is a cultural divide between computer scientists and biologists that needs to be addressed. The two disciplines used to be quite unrelated but many new research areas have arisen from their synergy. We selectively review two multi-disciplinary problems: dealing with contamination in sequencing data repositories and improving software using biology inspired evolutionary computing. Through several examples, we show that ideas from biology may result in optimised code and provide surprising improvements that overcome challenges in speed and quality trade-offs. On the other hand, development of computational methods is essential for maintaining contamination free databases. Computer scientists and biologists must always be sceptical of each others data, just as they would be of their own

Evaluation of genetic improvement tools for improvement of non-functional properties of software

Genetic improvement (GI) improves both functional properties of software, such as bug repair, and non-functional properties, such as execution time, energy consumption, or source code size. There are studies summarising and comparing GI tools for improving functional properties of software; however there is no such study for improvement of its non-functional properties using GI. Therefore, this research aims to survey and report on the existing GI tools for improvement of non-functional properties of software. We conducted a literature review of available GI tools, and ran multiple experiments on the found open-source tools to examine their usability. We applied a cross-testing strategy to check whether the available tools can work on different programs. Overall, we found 63 GI papers that use a GI tool to improve nonfunctional properties of software, within which 31 are accompanied with open-source code. We were able to successfully run eight GI tools, and found that ultimately only two ---Gin and PyGGI--- can be readily applied to new general software

Evolving AVX512 Parallel C Code Using GP

Using 512 bit Advanced Vector Extensions, previous development history and Intel documentation, BNF grammar based genetic improvement automatically ports RNAfold to AVX, giving up to a 1.77 fold speed up. The evolved code pull request is an accepted GI software maintenance update to bioinformatics package ViennaRNA

Evolving better RNAfold structure prediction

Grow and graft genetic programming (GGGP) evolves more than 50000 parameters in a state-of-the-art C program to make functional source code changes which give more accurate predictions of how RNA molecules fold up. Genetic improvement updates 29% of the dynamic programming free energy model parameters. In most cases (50.3%) GI gives better results on 4655 known secondary structures from RNA_STRAND (29.0% are worse and 20.7% are unchanged). Indeed it also does better than parameters recommended by Andronescu, M., et al.: Bioinformatics 23(13) (2007) i19–i28

Optimizing T Cell Manufacturing and Quality Using Functionalized Degradable Microscaffolds

Adoptive cell therapy using chimeric antigen receptor (CAR) T cells have shown promise in treating cancer, but manufacturing large numbers of high quality cells remains challenging. Currently approved T cell expansion technologies involve anti-CD3 and anti-CD28 antibodies, usually mounted on magnetic beads. This method fails to recapitulate many key signals found in vivo and is also heavily licensed by a few companies, limiting its long-term usefulness to manufactures and clinicians. Furthermore, highly potent, anti-tumor T cells are generally less-differentiated subtypes such as central memory and stem memory T cells. Despite this understanding, little has been done to optimize T cell expansion for generating these subtypes, including measurement and feedback control strategies that are necessary for any modern manufacturing process. The goal of this dissertation was to develop a microcarrier-based degradable microscaffold (DMS) T cell expansion system and determine biologically-meaningful critical quality attitudes and critical process parameters that could be used to optimize for highly-potent T cells. We developed and characterized the DMS system, including quality control steps. We also demonstrated the feasibility of expanding high-quality T cells. We used Design of Experiments methodology to optimize the DMS platform, and we developed a computational pipeline to identify and model the effects of measurable critical quality attributes and critical process parameters on the final product. Finally, we demonstrated the effectiveness of the DMS platform in vivo. This thesis lays the groundwork for a novel T cell expansion method which can be utilized at scale for clinical trials and beyond.Ph.D

Automated development of clinical prediction models using genetic programming

Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Genetic programming is a general methodology, the specific implementation of which requires development of several different specific elements such as problem representation, fitness, selection and genetic variation. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this work was to develop a genetic programming approach for survival analysis and demonstrate its utility for the automatic development of clinical prediction models using cardiovascular disease as a case study. We developed a tree-based untyped steady-state genetic programming approach for censored longitudinal data, comparing its performance to the de facto statistical method—Cox regression—in the development of clinical prediction models for the prediction of future cardiovascular events in patients with symptomatic and asymptomatic cardiovascular disease, using large observational datasets. We also used genetic programming to examine the prognostic significance of different risk factors together with their non-linear combinations for the prognosis of health outcomes in cardiovascular disease. These experiments showed that Cox regression and the developed steady-state genetic programming approach produced similar results when evaluated in common validation datasets. Despite slight relative differences, both approaches demonstrated an acceptable level of discriminative and calibration at a range of times points. Whilst the application of genetic programming did not provide more accurate representations of factors that predict the risk of both symptomatic and asymptomatic cardiovascular disease when compared with existing methods, genetic programming did offer comparable performance. Despite generally comparable performance, albeit in slight favour of the Cox model, the predictors selected for representing their relationships with the outcome were quite different and, on average, the models developed using genetic programming used considerably fewer predictors. The results of the genetic programming confirm the prognostic significance of a small number of the most highly associated predictors in the Cox modelling; age, previous atherosclerosis, and albumin for secondary prevention; age, recorded diagnosis of ’other’ cardiovascular disease, and ethnicity for primary prevention in patients with type 2 diabetes. When considered as a whole, genetic programming did not produce better performing clinical prediction models, rather it utilised fewer predictors, most of which were the predictors that Cox regression estimated be most strongly associated with the outcome, whilst achieving comparable performance. This suggests that genetic programming may better represent the potentially non-linear relationship of (a smaller subset of) the strongest predictors. To our knowledge, this work is the first study to develop a genetic programming approach for censored longitudinal data and assess its value for clinical prediction in comparison with the well-known and widely applied Cox regression technique. Using empirical data this work has demonstrated that clinical prediction models developed by steady-state genetic programming have predictive ability comparable to those developed using Cox regression. The genetic programming models were more complex and thus more difficult to validate by domain experts, however these models were developed in an automated fashion, using fewer input variables, without the need for domain specific knowledge and expertise required to appropriately perform survival analysis. This work has demonstrated the strong potential of genetic programming as a methodology for automated development of clinical prediction models for diagnostic and prognostic purposes in the presence of censored data. This work compared untuned genetic programming models that were developed in an automated fashion with highly tuned Cox regression models that was developed in a very involved manner that required a certain amount of clinical and statistical expertise. Whilst the highly tuned Cox regression models performed slightly better in validation data, the performance of the automatically generated genetic programming models were generally comparable. The comparable performance demonstrates the utility of genetic programming for clinical prediction modelling and prognostic research, where the primary goal is accurate prediction. In aetiological research, where the primary goal is to examine the relative strength of association between risk factors and the outcome, then Cox regression and its variants remain as the de facto approach

Functional Tissue Engineering of the Healing Anterior Cruciate Ligament: A Combined Experimental and Computational Approach

The anterior cruciate ligament (ACL) is the most important knee stabilizer and is frequently injured during sports and work related activities. Unfortunately, midsubstance ACL ruptures have a limited healing capacity. As such, surgical reconstruction using soft tissue autografts is often performed. However, long-term follow-up studies have revealed that 20-25% of patients had a less than satisfactory outcome. These negative results have renewed clinical interests in healing of a torn ACL by means of biological stimulation. Thus, there is a need for basic science studies in order to better understand such an approach and also to logically develop an effective functional tissue engineering (FTE) treatment for an injured ACL. The overall objective of this dissertation was to evaluate the positive impact of biological and mechanical augmentation on the healing of the ACL using a combined experimental and computational approach. The ability of an extracellular matrix (ECM) bioscaffold in combination with an ECM hydrogel to enhance ACL healing following suture repair was first demonstrated in the goat model. At 12 weeks of healing, ECM-treatment led to an increase in neo-tissue formation as well as improved biomechanical properties of the healing ACL compared to suture repair alone. Second, as the healing process of the ACL was relatively slow even with ECM treatment, mechanical augmentation to better restore initial joint stability was required. Therefore, a suture augmentation procedure was developed, and improved joint function was achieved versus suture repair alone at the time of surgery. Further, there was increased tissue formation and improved biomechanical properties of the healing ACL at 12 weeks of healing. Finally, as a step toward predicting long-term outcomes following these biological and mechanical augmentation procedures, a preliminary mathematical model was developed to describe the remodeling process of healing ligaments. The results of this work can now be used to guide future experiments using FTE treatments to enhance ACL healing. With a sound scientific basis, it is hoped that such exciting new technologies could then be translated into the clinical arena to improve patient outcome following ACL injuries