Field-control, phase-transitions, and life's emergence

Instances of critical-like characteristics in living systems at each organizational level as well as the spontaneous emergence of computation (Langton), indicate the relevance of self-organized criticality (SOC). But extrapolating complex bio-systems to life's origins, brings up a paradox: how could simple organics--lacking the 'soft matter' response properties of today's bio-molecules--have dissipated energy from primordial reactions in a controlled manner for their 'ordering'? Nevertheless, a causal link of life's macroscopic irreversible dynamics to the microscopic reversible laws of statistical mechanics is indicated via the 'functional-takeover' of a soft magnetic scaffold by organics (c.f. Cairns-Smith's 'crystal-scaffold'). A field-controlled structure offers a mechanism for bootstrapping--bottom-up assembly with top-down control: its super-paramagnetic components obey reversible dynamics, but its dissipation of H-field energy for aggregation breaks time-reversal symmetry. The responsive adjustments of the controlled (host) mineral system to environmental changes would bring about mutual coupling between random organic sets supported by it; here the generation of long-range correlations within organic (guest) networks could include SOC-like mechanisms. And, such cooperative adjustments enable the selection of the functional configuration by altering the inorganic network's capacity to assist a spontaneous process. A non-equilibrium dynamics could now drive the kinetically-oriented system towards a series of phase-transitions with appropriate organic replacements 'taking-over' its functions.Comment: 54 pages, pdf fil

Doctor of Philosophy

dissertationBiomedical data are a rich source of information and knowledge. Not only are they useful for direct patient care, but they may also offer answers to important population-based questions. Creating an environment where advanced analytics can be performed against biomedical data is nontrivial, however. Biomedical data are currently scattered across multiple systems with heterogeneous data, and integrating these data is a bigger task than humans can realistically do by hand; therefore, automatic biomedical data integration is highly desirable but has never been fully achieved. This dissertation introduces new algorithms that were devised to support automatic and semiautomatic integration of heterogeneous biomedical data. The new algorithms incorporate both data mining and biomedical informatics techniques to create "concept bags" that are used to compute similarity between data elements in the same way that "word bags" are compared in data mining. Concept bags are composed of controlled medical vocabulary concept codes that are extracted from text using named-entity recognition software. To test the new algorithm, three biomedical text similarity use cases were examined: automatically aligning data elements between heterogeneous data sets, determining degrees of similarity between medical terms using a published benchmark, and determining similarity between ICU discharge summaries. The method is highly configurable and 5 different versions were tested. The concept bag method performed particularly well aligning data elements and outperformed the compared algorithms by iv more than 5%. Another configuration that included hierarchical semantics performed particularly well at matching medical terms, meeting or exceeding 30 of 31 other published results using the same benchmark. Results for the third scenario of computing ICU discharge summary similarity were less successful. Correlations between multiple methods were low, including between terminologists. The concept bag algorithms performed consistently and comparatively well and appear to be viable options for multiple scenarios. New applications of the method and ideas for improving the algorithm are being discussed for future work, including several performance enhancements, configuration-based enhancements, and concept vector weighting using the TF-IDF formulas

The Sound of the hallmarks of cancer

The objective of this research is to create a mixed portfolio of data-driven composition and performance interfaces, fixed Electroacoustic/Computer music compositions, and live-improvised musical and audiovisual works reflecting cancer as a disease. The main methodology in generating the raw sonic material is the sonification of high-throughput, protein/RNA fold-change data, derived from the bio- molecular research of cancer cells. This data and relevant insight into the field are obtained as part of a collaboration with Barts Cancer Institute, in London, UK. Furthermore, for the purpose of musical effectiveness and reaching wider audiences, a focus has been placed on balancing the use of data-driven sonic material with composer-driven musical choices, by drawing upon the narrative of the Hallmarks of Cancer (Hanahan and Weinberg, 2011) which is a widely accepted conceptual framework in the field of cancer research for understanding the various biomolecular processes responsible for causing cancer. This method is adopted in order to inspire musical form, and guide some of the syntactic and aesthetic choices within both fixed and improvised works. In addition, this research also reflects upon the use of data sonification as an artistic tool and practice, while also addressing the contradictions and contention that arise as a result of scientific aims and expectations regarding sonification, resulting in a proposed original model for framing and classifying artistic works incorporating this approach

Understanding the Structure-Function Relationship in Peptide-Enabled High Entropy Alloy Nanocatalysts

The structural complexity in high entropy alloy nanocatalysts (HEAs), afforded by the homogeneous mixing of five or more elements, has resulted in a limited understanding about the origin of their promising electrocatalytic properties. This thesis investigates the structure-function relationship in HEAs using advanced material characterization techniques. At first, a methodology for resolving the atomic-scale structure of peptide-enabled HEAs was developed using high-energy X-ray diffraction (HE-XRD) coupled with atomic pair distribution function (PDF) and reverse Monte Carlo (RMC) simulations, yielding structure models over the length scale of HEAs. Coordination analysis of the structure models revealed a multifunctional interplay of geometric and electronic attributes of surface atoms in HEAs that was responsible for the catalytic activity enhancement during the methanol electrooxidation reaction. Using the methodology for resolving the atomic scale structure of HEAs and peptide sequence engineering, the structure-function relationship of model PtPdAuCoSn HEAs during ethanol electrooxidation reaction (EOR) was studied. Compositional analysis of the PtPdAuCoSn HEA structure models revealed distinct miscibility characteristics that were attributed to the unique biotic-abiotic interactions. Analysis of the structure models identified the rapid dehydrogenation of CH3CHO intermediate into CH3COads in an optimized adsorption configuration as the contributing factor for the high selectivity towards CH3COO- in PtPdAuCoSn HEAs. Armed with these insights, a study was designed for understanding the effect of changing the concentration of Pt in the structure-function relationship of PtPdAuCoSn HEAs using spatiotemporal structural insights from in-situ PDF. The structure models demonstrated a degree of metastability as a function of their corresponding configurational entropy. Analysis of the structure models revealed that high selectivity towards CH3COO- in PtPdAuCoSn HEAs during EOR originates from the enhanced distribution of Pd and Co surface atoms. In summary, this thesis uses atomic PDF and RMC simulations to draw structure-function correlations in HEAs, presenting a path forward for developing strategies for the rational design of HEAs. Through collaborative efforts from theoreticians and experimentalists, the methodology presented here can form the basis for accelerating the discovery of promising HEA configurations for emerging electrocatalytic applications

Disordered Proteins: Connecting Sequences to Emergent Properties

Many IDPs participate in coupled folding and binding reactions and form alpha helical structures in their bound complexes. Alanine, glycine, or proline scanning mutagenesis approaches are often used to dissect the contributions of intrinsic helicities to coupled folding and binding. These experiments can yield confounding results because the mutagenesis strategy changes the amino acid compositions of IDPs. Therefore, an important next step in mutagenesis-based approaches to mechanistic studies of coupled folding and binding is the design of sequences that satisfy three major constraints. These are (i) achieving a target intrinsic alpha helicity profile; (ii) fixing the positions of residues corresponding to the binding interface; and (iii) maintaining the native amino acid composition. Here, we report the development of a Genetic Algorithm for Design of Intrinsic secondary Structure (GADIS) for designing sequences that satisfy the specified constraints. We describe the algorithm and present results to demonstrate the applicability of GADIS by designing sequence variants of the intrinsically disordered PUMA system that undergoes coupled folding and binding to Mcl-1. Our sequence designs span a range of intrinsic helicity profiles. The predicted variations in sequence-encoded mean helicities are tested against experimental measurements.There is a significant collection of proteins with repeating blocks of oppositely charged residues where the consensus sequence is a block of four Glu residues followed by a block of four Lys or Arg residues, (Glu4(Lys/Arg)4)n. These proteins have been experimentally shown to form long single alpha helices (SAHs) under biologically relevant conditions. However, these results are confounding to disorder predictors and to certain atomistic simulations in that both predict these sequences to be strongly disordered. The current working hypothesis is that SAHs are stabilized by i:i+4 salt bridges between opposite charges in consecutive helical turns. We test the merits of this hypothesis to understand the sequence-encoded preference for SAHs and the logic behind the failure of certain atomistic simulations in anticipating the preference for stable SAHs.In simulations with fixed charges the favorable free energy of solvation of charged residues and the associated loss of sidechain entropy hinders the formation of SAHs. We proposed that alterations to charge states induced by sequence context might play an important role in stabilizing SAHs. We tested this hypothesis using a (Glu4Lys4)n repeat protein and a simulation strategy that permits the substitution of charged residues with neutralized protonated or deprotonated variants of Glu / Lys. Our results predict that stable SAH structures derive from the neutralization of approximately half the Glu residues. These findings explain experimental observations and also provide a coherent rationale for the failure of simulations based on fixed charge models. Large-scale sequence analysis reveals that naturally occurring sequences often include defects in charge patterns such as Gln or Ala substitutions. This sequence-encoded incorporation of uncharged residues combined with neutralization of charged residues might tilt the balance toward alpha helical conformations.Micron-sized, non-membrane bound cellular bodies can form as the result of collective interactions between modules of distinct multidomain proteins. Li et al. have examined the phase diagrams that result for polymers of SH3 domains and proline-rich modules (PRMs) while varying the number of interacting domains. It is noteworthy that flexible, intrinsically disordered linkers connect the interacting units within each polymer. Conventional wisdom holds that linkers play a passive role in determining the phase behavior of multidomain proteins that undergo phase separations. Here, we ask if this view is accurate. The motivation for our work comes from recent studies that have uncovered a rich diversity of composition-to-conformation and sequence-to-conformation relationships for intrinsically disordered proteins. The central finding is that disordered regions of proteins have distinct sequence-encoded conformational preferences. Accordingly, we reasoned that the conformational properties of linkers might be a contributing factor, in addition to polyvalency, to the phase behavior of multidomain proteins.We have developed and deployed a three-dimensional lattice model to arrive at a predictive framework to query the effects of linkers on the phase diagrams of polyvalent systems. We find that the critical concentration for phase transition can be influenced by the conformational properties of linkers. Specifically, our results show that linkers modulate the cooperative binding between domains of polymers that are already bound together. Depending on their conformational properties, linkers can also block access to the binding domains via excluded volume effects. Additionally, we find that the properties of the linkers can lead to controls over the mixing of proteins in these bodies. Specifically, we find that there are large ranges of parameters for three protein systems where the bodies isolate specific proteins to different regions of the bodies instead of uniformly mixing them. This result is validated by recent findings of organization inside some observed bodies

Deep Functional Mapping For Predicting Cancer Outcome

The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network. In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents

A collective intelligence framework for in silico representations of biomolecules and their activities.

The novel framework proposed in this thesis offers great potential for modelling the multi scale adaptive dynamics from molecules to cell at the physiological timescale. Most approaches for modelling biological phenomena focus on studies based on a specific instance of life, where specific biological problems are analysed. Mechanistic models based on universal principles will facilitate in developing general models for wider application in systems biology. The aim of the thesis is to investigate best approaches in representing biological complexity from molecules to cells and developing computational approaches to bring abstract theories to practical use by: (i) Identifying the major biomolecular self organising mechanism. (ii) Using a bottom-up integrative approach to model the internal organisation of the biological cell. (iii) Develop a Collective Intelligence based cell modelling and simulation environment to conduct analysis studies. This thesis argues that a system theoretic approach based on Collective Intelligence where the concepts of self organisation and emergence underlie the approach is ideal to represent the multi scale and multi objective nature of the biological cell from the bottom up. This thesis proposes a Collective Intelligence based cell modelling and simulation environment to conduct analysis studies on the collective behaviour of biomolecules

Supercooling as an Alternative to Chemical Cryopreservation and Implications for Nuclear bodies

De fleste analyser som benyttes i dag for å studere genomet og dets romlige organisering er ikke i stand til å identifisere genomiske områder som er midlertidig forbundet med en cellekjernes kjernelegemer i rom og tid. Dette skyldes at de eksperimentelle forholdene som blir brukt endrer det kjemiske eller fysiske miljøet i eller rundt genomet og påvirker den nært forbundne og følsomme strukturen til kjernelegemene. Denne avhandlingen undersøker om dyp underkjøling uten isdannelse kan bevare den strukturelle integriteten til isolerte hepatocytt-cellekjerner fra atlanterhavslaks (Salmo salar), og et kjernelegeme til stede i dem kalt nukleolus. Funnene rapportert i denne avhandlingen antyder at dyp underkjøling kan brukes til å lagre cellekjerner av høy kvalitet uten ekstra kryobeskyttelse ned til −15.3oC, uten observerbar forringelse etter >2 ukers lagring. Flere andre observasjoner gjort i forbindelse med dette arbeidet kan danne grunnlaget for videre vitenskapelige undersøkelser. Av dem er det verdt å bemerke en mulig observasjon av en underkjøling-fremkalt overgang for nukleolus, fra en miljøfølsom struktur, til en struktur som reagerer lite på ytre påvirkning. Hvis denne underkjøling-fremkalte effekten, som må bekreftes i andre studier, representerer et generelt fenomen for strukturer beriket med proteiner uten veldefinert struktur, vil dette trolig ha implikasjoner for studiet av også andre kjernelegemer.Most of the assays used today to study the genome and its physical topography, cannot identify the spatiotemporal genomic regions associated with the majority of different nuclear bodies. This is because the experimental conditions commonly used in these assays alter the chemical or physical environment around the genome and influence the closely linked and sensitive structure of nuclear bodies. This thesis explores whether supercooling without ice formation could preserve the structural integrity of isolated nuclei from Atlantic salmon (Salmo salar) hepatocytes, and a nuclear body contained within them, the nucleolus. The findings reported in this thesis suggests that supercooling can be used to store high-quality nuclei without additional cryoprotectants down to −15.3oC, with unnoticeable deterioration after >2 weeks storage. Several other observations during this work could form the basis for new scientific enquiries. The most notable being the possible observation of a supercooling-induced transition of nucleoli from an environmental sensitive structure to a largely insensitive structure. If the supercooling-induced effect, which needs to be confirmed in further studies, represent a general phenomenon for structures enriched in intrinsically disordered proteins, this would likely have implications for the study of other nuclear bodies

Doctor of Philosophy

dissertationThe unstable expansion of the polyglutamine (polyQ) tract is a critical factor in the pathogenic pathway of at least ten neurodegenerative diseases, including Huntington's disease, spinal and bulbar muscular atrophy (SBMA), dentatorubral-pallidoluysian atrophy (DRPLA), and seven spinocerebellar ataxias, all of which are termed as polyglutamine diseases. One less understood but common feature of polyQ diseases is polyQ protein aggregation. This dissertation explores the protein folding, hydrogen bonding, and water accessibility changes which are induced by the enlargement of the polyQ tract using advanced informatics and computational methods, including protein 3D structure modeling and molecular dynamics simulations. This dissertation also demonstrates that these state-of-the-art computational and informatics methods are powerful tools to provide useful insights into protein aggregation in polyQ diseases. The enlargement of polyQ segments affects both local and global structures of polyQ proteins as well as their water-accessibility, hydrogen bond patterns, and other structural characteristics. Results from both isolated polyQ and polyQ segments in the context of ataxin-2 and ataxin-3 show that the polyQ tracts increasingly prefer self-interaction as the lengths of the tracts increase, indicating an increased tendency toward aggregation among larger polyQ tracts. These results provide new insights into possible pathogenic mechanisms of polyQ diseases based solely on the increased propensity toward polyQ aggregation and suggest that the modulation of solvent-polyQ interaction may be a possible therapeutic strategy for treating polyQ diseases. The analysis pipeline designed and used in this study is an effective way to study the molecular mechanism of polyQ diseases, and can be generalized to study other diseases associated with the protein conformation changes, such as Parkinson's disease, Alzheimer's disease, and cancer