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Abstract. Artificial biochemical networks (ABNs) are computational models inspired by the biochemical networks which underlie the cellular activities of biological organisms. This paper shows how evolved ABNs may be used to control chaotic dynamics in both discrete and continuous dynamical systems, illustrating that ABNs can be used to represent complex computational behaviours within evolutionary algorithms. Our results also show that performance is sensitive to model choice, and suggest that conservation laws play an important role in guiding search.

Animal models of human disease provide an in vivo system that can reveal molecular mechanisms by which mutations cause pathology, and, moreover, have the potential to provide a valuable tool for drug development. Here we have developed a zebrafish model of Parkinson's disease (PD) together with a novel method to screen for movement disorders in adult fish, pioneering a more efficient drug testing route. Mutation of the PARK7 gene (DJ-1) is known to cause monogenic autosomal recessive PD in humans and, using CRISPR/Cas9 gene editing we have generated a DJ-1 loss of function zebrafish with molecular hallmarks of PD. To establish whether there is a human-relevant parkinsonian phenotype in our model, we have adapted proven tools used to diagnose PD in clinics and have developed a novel and unbiased computational method to classify movement disorders in adult zebrafish. Using high-resolution video capture and machine learning, we have extracted novel features of movement from continuous data streams and used an evolutionary algorithm (EA) to classify parkinsonian fish. This method will be widely applicable for assessing zebrafish models of human motor diseases and provide a valuable asset for the therapeutics pipeline. In addition, interrogation of RNA-seq data indicate metabolic reprogramming of brains in the absence of DJ-1, adding to growing evidence that disruption of bioenergetics is a key feature of neurodegeneration.

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Cancer poses a massive health burden with incidence rates expected to double globally over the next decade. In the United Kingdom screening programmes exists for cervical, breast, and colorectal cancer. The ability to screen individuals for solid malignant tumours using only a peripheral blood sample would revolutionise cancer services and permit early diagnosis and intervention. Raman spectroscopy interrogates native biochemistry through the interaction of light with matter, producing a high definition biochemical 'fingerprint' of the target material. This paper explores the possibility of using Raman spectroscopy to discriminate between cancer and noncancer patients through a peripheral blood sample. Forty blood samples were obtained from patients with Head and Neck cancer and patients with respiratory illnesses to act as a positive control. Raman spectroscopy was carried out on all samples with the resulting spectra being used to build a classifier in order to distinguish between the cancer and respiratory patients' spectra; firstly using principal component analysis (PCA)/linear discriminant analysis (LDA), and secondly with a genetic evolutionary algorithm. The PCA/LDA classifier gave a 65% sensitivity and specificity for discrimination between the cancer and respiratory groups. A sensitivity score of 75% with a specificity of 75% was achieved with a 'trained' evolutionary algorithm. In conclusion this preliminary study has demonstrated the feasibility of using Raman spectroscopy in cancer screening and diagnostics of solid tumours through a peripheral blood sample. Further work needs to be carried out for this technique to be implemented in the clinical setting.