Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation

A novel approach to hardware fault tolerance is demonstrated that takes inspiration from the human immune system as a method of fault detection. The human immune system is a remarkable system of interacting cells and organs that protect the body from invasion and maintains reliable operation even in the presence of invading bacteria or viruses. This paper seeks to address the field of electronic hardware fault tolerance from an immunological perspective with the aim of showing how novel methods based upon the operation of the immune system can both complement and create new approaches to the development of fault detection mechanisms for reliable hardware systems. In particular, it is shown that by use of partial matching, as prevalent in biological systems, high fault coverage can be achieved with the added advantage of reducing memory requirements. The development of a generic finite-state-machine immunization procedure is discussed that allows any system that can be represented in such a manner to be "immunized" against the occurrence of faulty operation. This is demonstrated by the creation of an immunized decade counter that can detect the presence of faults in real tim

On the origin of synthetic life: Attribution of output to a particular algorithm

With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to gain an ability to distinguish between natural and artificial life forms. In this paper, we address this challenge as a generalized version of Darwin\u27s original problem, which he so brilliantly described in On the Origin of Species. After formalizing the problem of determining the samples\u27 origin, we demonstrate that the problem is in fact unsolvable. In the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal, both explanations are equality likely. Our results should attract attention of astrobiologists and scientists interested in developing a more complete theory of life, as well as of AI-Safety researchers

On the origin of synthetic life: Attribution of output to a particular algorithm

With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to gain an ability to distinguish between natural and artificial life forms. In this paper, we address this challenge as a generalized version of Darwin\u27s original problem, which he so brilliantly described in On the Origin of Species. After formalizing the problem of determining the samples\u27 origin, we demonstrate that the problem is in fact unsolvable. In the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal, both explanations are equality likely. Our results should attract attention of astrobiologists and scientists interested in developing a more complete theory of life, as well as of AI-Safety researchers

Developing biocontainment strategies to suppress transgene escape via pollen dispersal from transgenic plants

Genetic engineering is important to enhance crop characteristics and certain traits. Genetically engineered crop cultivation brings environmental and ecological concerns with the potential of unwanted transgene escape and introgression. Transgene escape has been considered as a major environmental and regulatory concern. This concern could be alleviated by appropriate biocontainment strategies. Therefore, it is important to develop efficient and reliable biocontainment strategies. Removing transgenes from pollen has been known to be the most environmentally friendly biocontainment strategy. A transgene excision vector containing a codon optimized serine resolvase CinH recombinase (CinH) and its recognition sites RS2 were constructed and transformed into tobacco (Nicotiana tabacum cv. Xanthi). In this system, the pollen-specific LAT52 promoter from tomato was employed to control the expression of CinH recombinase. Loss of expression of a green fluorescent protein (GFP) gene under the control of the LAT59 promoter from tomato was used as an indicator of transgene excision. Efficiency of transgene excision from pollen was determined by flow cytometry (FCM)-based pollen screening. While a transgenic event in the absence of CinH recombinase contained about 70% of GFP-synthesizing pollen, three single-copy transgene events contained less than 1% of GFP-synthesizing pollen based on 30,000 pollen grains analyzed per event. This suggests that CinH-RS2 recombination system could be effectively utilized for transgene biocontainment. A novel approach for selective male sterility in pollen was developed and evaluated as a biocontainment strategy. Overexpression of the EcoRI restriction endonuclease caused pollen ablation and/or infertility in tobacco, but exhibited normal phenotypes when compared to non-transgenic tobacco. Three EcoRI contained 0% GFP positive pollen, while GFP control plants contained 64% GFP positive pollen based on 9,000 pollen grains analyzed by flow cytometry-based transgenic pollen screening method. However, seven EcoRI events appeared to have 100% efficiency on selective male sterility based on the test-crosses. The results suggested that this selective male sterility could be used as a highly efficient and reliable biocontainment strategy for genetically engineered crop cultivation

Measurement of bioprocess containment by quantitative polymerase chain reaction

This thesis describes the development and application of a method for the measurement of the release of genetically modified micro-organisms from large scale bioprocesses. Polymerase chain reaction (PCR) assays for two E. coli K-12 strains have been shown to be specific for the target strain and have sufficiently low limits of detection (less than 50 cells per PCR) for monitoring of bioprocess release. A quantitative PCR assay, using a competitive internal standard, for one E. coli strain allows measurement of the concentration of the bacteria over a range of up to 6 orders of magnitude with a measurement error of ±0.11 logs. This method has been applied to samples taken from an Aerojet General Cyclone air sampling device allowing the determination of the number of whole cells of the target organism in a sampled aerosol. Using this method, good correlation has been observed between the number of cells released by atomisation into a fixed, contained volume and the number of cells captured and enumerated. Aspects of large scale fermentation, homogenisation and centrifugation unit operations have been studied to determine the effectiveness of their containment. Airborne release of process micro-organisms has been detected in some instances, but the scale of the release was generally found to be small considering the total biomass involved in the bioprocess. Implications of the methodology and the findings from model and case studies on current engineering practice and bioprocess risk assessment are discussed. Areas for further improvement of the method and applications outside of bioprocess containment validation are identified

DETECTING GENETIC ENGINEERING WITH A KNOWLEDGE-RICH DNA SEQUENCE CLASSIFIER

Detecting evidence of genetic engineering in the wild is a problem of growing importance for biosecurity, provenance, and intellectual property rights. This thesis describes a computational system designed to detect engineering from DNA sequencing of biological samples and presents its performance on fully blinded test data. The pipeline builds on existing computational resources for metagenomics, including methods that use the full set of reference genomes deposited in GenBank. Starting from raw reads generated from short-read sequencers, the dominant host species are identified by k-mer analysis. Next, all the sequencing reads are mapped to the imputed host strain; those reads that do not map are retained as suspicious. Suspicious reads are de novo assembled to suspicious contigs, followed by sequence alignment against the NCBI non-redundant nucleotide database to annotate the engineered sequence and to identify whether the engineering is in a plasmid or is integrated into the host genome. Our initial system applied to blinded samples provides excellent identification of foreign gene content, the changes most likely to be functional. We have less ability to detect functional structural variants and small indels and SNPs produced by genetic engineering but which are more difficult to distinguish from natural variation. Future work will focus on improved methods for detecting synonymous recoding, used to introduce watermarks and for compatibility with synthesis and assembly methods, for using long read sequence data, and for distinguishing engineered sequence from natural variation

ToxDL : deep learning using primary structure and domain embeddings for assessing protein toxicity

Motivation: Genetically engineering food crops involves introducing proteins from other species into crop plant species or modifying already existing proteins with gene editing techniques. In addition, newly synthesized proteins can be used as therapeutic protein drugs against diseases. For both research and safety regulation purposes, being able to assess the potential toxicity of newly introduced/synthesized proteins is of high importance. Results: In this study, we present ToxDL, a deep learning-based approach for in silico prediction of protein toxicity from sequence alone. ToxDL consists of (i) a module encompassing a convolutional neural network that has been designed to handle variable-length input sequences, (ii) a domain2vec module for generating protein domain embeddings and (iii) an output module that classifies proteins as toxic or non-toxic, using the outputs of the two aforementioned modules. Independent test results obtained for animal proteins and cross-species transferability results obtained for bacteria proteins indicate that ToxDL outperforms traditional homology-based approaches and state-of-the-art machine-learning techniques. Furthermore, through visualizations based on saliency maps, we are able to verify that the proposed network learns known toxic motifs. Moreover, the saliency maps allow for directed in silico modification of a sequence, thus making it possible to alter its predicted protein toxicity

Characterizing and analyzing disease-related omics data using network modeling approaches

Systems biology explores how the components that constitute a biological system interact with each other to produce biological phenotypes. A number of tools for comprehensive and high-throughput measurements of DNA/RNA, protein and metabolites have been developed. Each of these technologies helps to characterize individual components of the genome, proteome or metabolome and offers a distinct perspective about the system structure. My dissertation aims to characterize and analyze multiple types of omics data using existing and novel network-based approaches to better understand disease development mechanisms and improve disease diagnosis and prognosis. The transcriptome reflects the expression level of mRNAs in single cells or a population of cells. Understanding the transcriptome is an essential part of understanding organism development and disease. The first part of my thesis work focused on analyzing transcriptome data to characterize aggressiveness and heterogeneity of human astrocytoma, the most common glioma with a strikingly high mortality rate. A large-scale global gene expression analysis was performed to analyze gene expression profiles representing hundreds of samples generated by oligonucleotide microarrays. I employed a combination of gene- and network-based approaches to investigate the genetic and biological mechanisms implicated in observed phenotypic differences. I observed increasing dysregulation with increasing tumor grade and concluded that transcriptomic heterogeneity, observed at the population scale, is generally correlated with increasingly aggressive phenotypes. Heterogeneity in high-grade astrocytomas also manifests as differences in clinical outcomes and significant efforts had been devoted to identify subtypes within high-grade astrocytomas that have large differences in prognosis. I developed an automated network screening approach which could identify networks capable of predicting subtypes with differential survival in high-grade astrocytomas. The proteome represents the translated product of the mRNA, and proteomics measurement provides a direct estimate of protein abundance. For the second part of my Ph.D. research, I analyzed mouse brain protein measurements collected by the iTRAQ technology to query and identify dynamically perturbed modules in progressive mouse models of glioblastoma. Network behavior changes in early, middle and late stages of tumor development in genetically engineered mouse were tracked and 19 genes were selected for further confirmation of their roles in glioblastoma progression. In addition to this specific application to mouse glioblastoma data, the general pipeline represented a novel effort to isolate pathway-level responses to perturbations (e.g., brain tumor formation and progression) from large-scale proteomics data and could be applied in analyzing proteomics data from a variety of different contexts. The metabolome reflects biological information related to biochemical processes and metabolic networks involving metabolites. Metabolomics data can give an instantaneous snapshot of the current state of the cell and thus offers a distinct view of the effects of diet, drugs and disease on the model organism. The third part of my thesis is dedicated to building and refining genome-scale in silico metabolic models for mouse, in order to investigate how the metabolic model responds differently under different conditions (e.g., diabetic vs. normal). This project was completed in two stages: first, I examined the state-of-art genome-scale mouse metabolic model, identified its limitations, and then improved and refined its functionality; second, I created the first liver-specific metabolic models from the generic mouse models by pruning reactions that lack genetic evidence of presence, and then adding liver-specific reactions that represent the characteristics and functions of the mouse liver. Finally, I reconstructed two liver metabolic models for mouse, with one for the normal (control) strain and one for mouse diabetic strains. These two models were compared physiologically to infer metabolic genes that were most impacted by the onset of diabetes

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson’s disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson’s by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way