Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system

Biology has taken strong steps towards becoming a computer science aiming at reprogramming nature after the realisation that nature herself has reprogrammed organisms by harnessing the power of natural selection and the digital prescriptive nature of replicating DNA. Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back. These concepts provide a means to a hierarchical organization thereby blurring previously clear-cut lines between concepts like matter and life, or between tumour types that are otherwise taken as different and may not have however a different cause. This does not diminish the properties of life or make its components and functions less interesting. On the contrary, this approach makes for a more encompassing and integrated view of nature, one that subsumes observer and observed within the same system, and can generate new perspectives and tools with which to view complex diseases like cancer, approaching them afresh from a software-engineering viewpoint that casts evolution in the role of programmer, cells as computing machines, DNA and genes as instructions and computer programs, viruses as hacking devices, the immune system as a software debugging tool, and diseases as an information-theoretic battlefield where all these forces deploy. We show how information theory and algorithmic programming may explain fundamental mechanisms of life and death.Comment: 30 pages, 8 figures. Invited chapter contribution to Information and Causality: From Matter to Life. Sara I. Walker, Paul C.W. Davies and George Ellis (eds.), Cambridge University Pres

Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly

Inspired by biology’s most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Analogous high-dimensional, highly interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically, we design a set of 917 DNA tiles that can self-assemble in three alternative ways such that competitive nucleation depends sensitively on the extent of colocalization of high-concentration tiles within the three structures. The system was trained in silico to classify a set of 18 grayscale 30 × 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy measurements during and after a 150 hour anneal established that all trained images were correctly classified, whereas a test set of image variations probed the robustness of the results. Although slow compared to previous biochemical neural networks, our approach is compact, robust and scalable. Our findings suggest that ubiquitous physical phenomena, such as nucleation, may hold powerful information-processing capabilities when they occur within high-dimensional multicomponent systems

Biological Nanowires: Integration of the silver(I) base pair into DNA with nanotechnological and synthetic biological applications

Modern computing and mobile device technologies are now based on semiconductor technology with nanoscale components, i.e., nanoelectronics, and are used in an increasing variety of consumer, scientific, and space-based applications. This rise to global prevalence has been accompanied by a similarly precipitous rise in fabrication cost, toxicity, and technicality; and the vast majority of modern nanotechnology cannot be repaired in whole or in part. In combination with looming scaling limits, it is clear that there is a critical need for fabrication technologies that rely upon clean, inexpensive, and portable means; and the ideal nanoelectronics manufacturing facility would harness micro- and nanoscale fabrication and self-assembly techniques. The field of molecular electronics has promised for the past two decades to fill fundamental gaps in modern, silicon-based, micro- and nanoelectronics; yet molecular electronic devices, in turn, have suffered from problems of size, dispersion and reproducibility. In parallel, advances in DNA nanotechnology over the past several decades have allowed for the design and assembly of nanoscale architectures with single-molecule precision, and indeed have been used as a basis for heteromaterial scaffolds, mechanically-active delivery mechanisms, and network assembly. The field has, however, suffered for lack of meaningful modularity in function: few designs to date interact with their surroundings in more than a mechanical manner. As a material, DNA offers the promise of nanometer resolution, self-assembly, linear shape, and connectivity into branched architectures; while its biological origin offers information storage, enzyme-compatibility and the promise of biologically-inspired fabrication through synthetic biological means. Recent advances in DNA chemistry have isolated and characterized an orthogonal DNA base pair using standard nucleobases: by bridging the gap between mismatched cytosine nucleotides, silver(I) ions can be selectively incorporated into the DNA helix with atomic resolution. The goal of this thesis is to explore how this approach to “metallize” DNA can be combined with structural DNA nanotechnology as a step toward creating electronically-functional DNA networks. This work begins with a survey of applications for such a transformative technology, including nanoelectronic component fabrication for low-resource and space-based applications. We then investigate the assembly of linear Ag+-functionalized DNA species using biochemical and structural analyses to gain an understanding of the kinetics, yield, morphology, and behavior of this orthogonal DNA base pair. After establishing a protocol for high yield assembly in the presence of varying Ag+ functionalization, we investigate these linear DNA species using electrical means. First a method of coupling orthogonal DNA to single-walled carbon nanotubes (SWCNTs) is explored for self-assembly into nanopatterned transistor devices. Then we carry out scanning tunneling microscope (STM) break junction experiments on short polycytosine, polycationic DNA duplexes and find increased molecular conductance of at least an order of magnitude relative to the most conductive DNA analog. With an understanding of linear species from both a biochemical and nanoelectronic perspective, we investigate the assembly of nonlinear Ag+-functionalized DNA species. Using rational design principles gathered from the analysis of linear species, a de novo mathematical framework for understanding generalized DNA networks is developed. This provides the basis for a computational model built in Matlab that is able to design DNA networks and nanostructures using arbitrary base parity. In this way, DNA nanostructures are able to be designed using the dC:Ag+:dC base pair, as well as any similar nucleobase or DNA-inspired system (dT:Hg2+:dT, rA:rU, G4, XNA, LNA, PNA, etc.). With this foundation, three general classes of DNA tiles are designed with embedded nanowire elements: single crossover Holliday junction (HJ) tiles, T-junction (TJ) units, and double crossover (DX) tile pairs and structures. A library of orthogonal chemistry DNA nanotechnology is described, and future applications to nanomaterials and circuit architectures are discussed

Mining SOM expression portraits: Feature selection and integrating concepts of molecular function

Background: 
Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of molecular function.

Results: 
Different expression scores based either on simple fold change-measures or on regularized Students t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues, as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and highly expressed genes using SOM data filtering. 

Conclusions:
The presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones

A FAST IMPLEMENTATION FOR CORRECTING ERRORS IN HIGH THROUGHPUT SEQUENCING DATA

ABSTRACT The impact of the next generation DNA sequencing technologies (NGS) produced a revolu­tion in biological research. New computational tools are needed to deal with the huge amounts of data they output. Significantly shorter length of the reads and higher per-base error rate compared with Sanger technology make things more difficult and still critical problems, such as genome assembly, are not satisfactorily solved. Significant efforts have been spent recently on software programs aimed at increasing the quality of the NGS data by correcting errors. The most accurate program to date is HiTEC and our contribution is providing a completely new implementation, HiTEC2. The new program is many times faster and uses much less space, while correcting more errors in the same number of iterations. We have eliminated the need of the suffix array data structure and the need of installing complicating statistical libraries as well, thus making HiTEC2 not only more efficient but also friendlier

Nucleic Acid Architectures for Therapeutics, Diagnostics, Devices and Materials

Nucleic acids (RNA and DNA) and their chemical analogs have been utilized as building materials due to their biocompatibility and programmability. RNA, which naturally possesses a wide range of different functions, is now being widely investigated for its role as a responsive biomaterial which dynamically reacts to changes in the surrounding environment. It is now evident that artificially designed self-assembling RNAs, that can form programmable nanoparticles and supra-assemblies, will play an increasingly important part in a diverse range of applications, such as macromolecular therapies, drug delivery systems, biosensing, tissue engineering, programmable scaffolds for material organization, logic gates, and soft actuators, to name but a few. The current exciting Special Issue comprises research highlights, short communications, research articles, and reviews that all bring together the leading scientists who are exploring a wide range of the fundamental properties of RNA and DNA nanoassemblies suitable for biomedical applications

The state of the art in the analysis of two-dimensional gel electrophoresis images

Software-based image analysis is a crucial step in the biological interpretation of two-dimensional gel electrophoresis experiments. Recent significant advances in image processing methods combined with powerful computing hardware have enabled the routine analysis of large experiments. We cover the process starting with the imaging of 2-D gels, quantitation of spots, creation of expression profiles to statistical expression analysis followed by the presentation of results. Challenges for analysis software as well as good practices are highlighted. We emphasize image warping and related methods that are able to overcome the difficulties that are due to varying migration positions of spots between gels. Spot detection, quantitation, normalization, and the creation of expression profiles are described in detail. The recent development of consensus spot patterns and complete expression profiles enables one to take full advantage of statistical methods for expression analysis that are well established for the analysis of DNA microarray experiments. We close with an overview of visualization and presentation methods (proteome maps) and current challenges in the field

Information-Directed Hybridization of Abiotic, Sequence-Defined Oligomers

The capacity for sequence-specific polymer strands to selectively assemble into intricate, folded structures and multimeric complexes relies upon the information borne by their residue sequences. Particularly suitable for the formation of multi-dimensional structures, nucleic acids have emerged as sophisticated nanoconstruction media where encoded sequences self-assemble in a designed manner through the gradual cooling of denatured and dissociated strands from raised temperatures. Unfortunately, the weakness of the hydrogen bonds holding the strands together affords nanoconstructs with thermal and mechanical instabilities. In contrast, molecular self-assembly employing dynamic covalent interactions has contributed to the improved mechanical and chemical stabilities of resultant structures. Nevertheless, compared with supramolecular chemistries, dynamic covalent interactions suffer from low dissociation rates, impeding rearrangement amongst the assembled components and often result in the kinetic trapping of non-equilibrium species. To overcome this limitation, molecular architectures are generally restricted to homo-functionalized constituents bearing few reactive sites or utilize harsh self-assembly conditions. This dissertation examines the deliberate equilibrium shifting of dynamic covalent interactions to fabricate sequence-selective molecular architectures with high degrees of functionalization. First, we explored the use of a Lewis acidic catalyst, scandium triflate, Sc(OTf)3, to affect the equilibrium of imine formation, a well-characterized dynamic covalent interaction. Here, high concentrations of scandium triflate, dissociated oligomeric-strands encoded with amine- and aldehyde-pendant group species. Upon removal of excess scandium triflate with a liquid-liquid extraction, the equilibrium was shifted as to promote imine-formation between complementary strands. Subsequent annealing of the self-assembly solutions at 70°C, enabling rearrangement and error-correction of out-of-registry or non-complementary sequences, afforded the simultaneous formation of three distinct information-bearing ladder species and a mechanism for information storage and retrieval of data by abiotic polymers. The information-directed self-assembly of encoded molecular ladders was further developed by incorporating an orthogonal reaction into the oligomeric strands to mimic the information dense, sequence-selective hybridization of DNA. Thus, the base-4 information-directed assembly of molecular ladders and grids bearing covalent bond-based rungs was demonstrated from encoded precursor strands using dual concurrent, orthogonal dynamic covalent interactions (i.e., amine/aldehyde and boronic acid/catechol condensation reactions). Additionally, the self-assembly of well-characterized ladder species employing the thermally-reversible Diels-Alder cycloaddition reaction was explored to establish a self-assembly mechanism requiring an external stimulus to alleviate or eliminate kinetic trapping. By utilizing furan-protected maleimide and furfurylamine residues, sequence-defined strands were synthesized simultaneously bearing both furan and maleimide species while precluding premature hybridization and self-assembled in an information-directed manner to form distinct ladder species using a temperature-mediated process. Finally, given the large-scale efforts underway to develop rapid SARS-CoV-2 (Severe Acute Respiratory Syndrome - coronavirus – 2) diagnostic tests, the fundamental principles of sequence-selective hybridization were applied to transform blood-typing tests into SARS-CoV-2 serology tests using robust gel card agglutination reactions in combination with easily prepared antibody-peptide bioconjugates.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162941/1/sleguiz_1.pd

Regional variation of microtubule flux reveals microtubule organization in the metaphase meiotic spindle

Continuous poleward movement of tubulin is a hallmark of metaphase spindle dynamics in higher eukaryotic cells and is essential for stable spindle architecture and reliable chromosome segregation. We use quantitative fluorescent speckle microscopy to map with high resolution the spatial organization of microtubule flux in Xenopus laevis egg extract meiotic spindles. We find that the flux velocity decreases near spindle poles by ∼20%. The regional variation is independent of functional kinetochores and centrosomes and is suppressed by inhibition of dynein/dynactin, kinesin-5, or both. Statistical analysis reveals that tubulin flows in two distinct velocity modes. We propose an association of these modes with two architecturally distinct yet spatially overlapping and dynamically cross-linked arrays of microtubules: focused polar microtubule arrays of a uniform polarity and slower flux velocities are interconnected by a dense barrel-like microtubule array of antiparallel polarities and faster flux velocities

Advance the DNA computing

It has been previously shown that DNA computing can solve those problems currently intractable on even the fastest electronic computers. The algorithm design for DNA computing, however, is not straightforward. A strong background in both the DNA molecule and computer engineering are required to develop efficient DNA computing algorithms. After Adleman solved the Hamilton Path Problem using a combinatorial molecular method, many other hard computational problems were investigated with the proposed DNA computer. The existing models from which a few DNA computing algorithms have been developed are not sufficiently powerful and robust, however, to attract potential users. This thesis has described research performed to build a new DNA computing model based on various new algorithms developed to solve the 3-Coloring problem. These new algorithms are presented as vehicles for demonstrating the advantages of the new model, and they can be expanded to solve other NP-complete problems. These new algorithms can significantly speed up computation and therefore achieve a consistently better time performance. With the given resource, these algorithms can also solve problems of a much greater size, especially as compared to existing DNA computation algorithms. The error rate can also be greatly reduced by applying these new algorithms. Furthermore, they have the advantage of dynamic updating, so an answer can be changed based on modifications made to the initial condition. This new model makes use of the huge possible memory by generating a ``lookup table'' during the implementation of the algorithms. If the initial condition changes, the answer changes accordingly. In addition, the new model has the advantage of decoding all the strands in the final pool both quickly and efficiently. The advantages provided by the new model make DNA computing an efficient and attractive means of solving computationally intense problems