BIOMOLECULE INSPIRED DATA SCIENCE

BIOMOLECULE INSPIRED DATA SCIENC

Evolution from the ground up with Amee – From basic concepts to explorative modeling

Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes

Electron paramagnetic resonance studies of spin-labelled ethidium bromide DNA interactions

Spin-Labelled Ethidium Bromide (SLEB) was prepared in order to study its interactions with natural DNA in the form of fibres . The technique of electron paramagnetic resonance was used in this thesis. Knowledge of the conformational transition pathway of natural DNA for given counterion concentration as a function of relative humidity was utilised in the study of effect DNA confomation on the binding of SLEB. To aid interpretation of the results the relevant background material was reviewed. In order to attempt to extract geometric information on binding computer ERR lineshape simulations were used. To facilitate this a microcomputer spectrometer control system was designed and implemented. This allowed spectra to be acquired in digital form and transfered to the mainframe computer. Two schemes for magnetic field control were investigated, one based on a commercial NMR magnetometer, and a superior pulsed NMR field locking magnetometer developed in this laboratory. In order to obtain lineshapes undistorted by dipolar broadening it is advantageous to use fibres with a high phosphate to drug ratio (P/D), however spectrometer sensitivity becomes a limiting factor. A review of noise in spectrometer systems is included. The use of a microwave low-noise preamplifer to reduce the system noise figure was investigated. An attempt to construct a loop-gap resonator was made and justified theoretically. A 35GHz spectrometer was constructed and a cavity designed and built to allow the humidity to be varied. The system was made compatible with the control system. Spectra recorded and simulated at this frequency should help confirm those obtained at 9GHz. The results obtained from P/D«70 fibres with a 0.5mM NaCl concentration show the SLEB is in a disordered state from 33% to 75% relative humidity. Spectral changes occur in the range 75% to 98% consistant with intercalation. In this humidity range a transition to the B-form is expected

Design Of Dna Strand Displacement Based Circuits

DNA is the basic building block of any living organism. DNA is considered a popular candidate for future biological devices and circuits for solving genetic disorders and several other medical problems. With this objective in mind, this research aims at developing novel approaches for the design of DNA based circuits. There are many recent developments in the medical field such as the development of biological nanorobots, SMART drugs, and CRISPR-Cas9 technologies. There is a strong need for circuits that can work with these technologies and devices. DNA is considered a suitable candidate for designing such circuits because of the programmability of the DNA strands, small size, lightweight, known thermodynamics, higher parallelism, and exponentially reducing the cost of synthesizing techniques. The DNA strand displacement operation is useful in developing circuits with DNA strands. The circuit can be either a digital circuit, in which the logic high and logic low states of the DNA strand concentrations are considered as the signal, or it can be an analog circuit in which the concentration of the DNA strands itself will act as the signal. We developed novel approaches in this research for the design of digital, as well as analog circuits keeping in view of the number of DNA strands required for the circuit design. Towards this goal in the digital domain, we developed spatially localized DNA majority logic gates and an inverter logic gate that can be used with the existing seesaw based logic gates. The majority logic gates proposed in this research can considerably reduce the number of strands required in the design. The introduction of the logic inverter operation can translate the dual rail circuit architecture into a monorail architecture for the seesaw based logic circuits. It can also reduce the number of unique strands required for the design into approximately half. The reduction in the number of unique strands will consequently reduce the leakage reactions, circuit complexity, and cost associated with the DNA circuits. The real world biological inputs are analog in nature. If we can use those analog signals directly in the circuits, it can considerably reduce the resources required. Even though analog circuits are highly prone to noise, they are a perfect candidate for performing computations in the resource-limited environments, such as inside the cell. In the analog domain, we are developing a novel fuzzy inference engine using analog circuits such as the minimum gate, maximum gate, and fan-out gates. All the circuits discussed in this research were designed and tested in the Visual DSD software. The biological inputs are inherently fuzzy in nature, hence a fuzzy based system can play a vital role in future decision-making circuits. We hope that our research will be the first step towards realizing these larger goals. The ultimate aim of our research is to develop novel approaches for the design of circuits which can be used with the future biological devices to tackle many medical problems such as genetic disorders

Structure determination of an HIV-1 RRE RNA-Rev peptide complex by NMR spectroscopy

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 1996.Includes bibliographical references (leaves 188-200).by John L. Battiste.Ph.D

Data Mining of Biomedical Databases

Data mining can be defined as the nontrivial extraction of implicit, previously unknown and potentially useful information from data. This thesis is focused on Data Mining in Biomedicine, representing one of the most interesting fields of application. Different kinds of biomedical data sets would require different data mining approaches. Two approaches are treated in this thesis, divided in two separate and independent parts. The first part deals with Bayesian Networks, representing one of the most successful tools for medical diagnosis and therapies follow-up. Formally, a Bayesian Network (BN) is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. An algorithm for Bayesian network structure learning that is a variation of the standard search-and-score approach has been developed. The proposed approach overcomes the creation of redundant network structures that may include non significant connections between variables. In particular, the algorithm finds which relationships between the variables must be prevented, by exploiting the binarization of a square matrix containing the mutual information (MI) among all pairs of variables. Four different binarization methods are implemented. The MI binary matrix is exploited as a pre-conditioning step for the subsequent greedy search procedure that optimizes the network score, reducing the number of possible search paths in the greedy search procedure. This approach has been tested on four different datasets and compared against the standard search-and-score algorithm as implemented in the DEAL package, with successful results. Moreover, a comparison among different network scores has been performed. The second part of this thesis is focused on data mining of microarray databases. An algorithm able to perform the analysis of Illumina microRNA microarray data in a systematic and easy way has been developed. The algorithm includes two parts. The first part is the pre-processing, characterized by two steps: variance stabilization and normalization. Variance stabilization has to be performed to abrogate or at least reduce the heteroskedasticity while normalization has to be performed to minimize systematic effects that are not constant among different samples of an experiment and that are not due to the factors under investigation. Three alternative variance stabilization strategies and three alternative normalization approaches are included. So, considering all the possible combinations between variance stabilization and normalization strategies, 9 different ways to pre-process the data are obtained. The second part of the algorithm deals with the statistical analysis for the differential expression detection. Linear models and empirical Bayes methods are used. The final result is the list of the microRNAs significantly differentially-expressed in two different conditions. The algorithm has been tested on three different real datasets and partially validated with an independent approach (quantitative real time PCR). Moreover, the influence of the use of different preprocessing methods on the discovery of differentially expressed microRNAs has been studied and a comparison among the different normalization methods has been performed. This is the first study comparing normalization techniques for Illumina microRNA microarray data

Role of the anisotropy in the interactions between nano- and micro-sized particles

The present Thesis focuses on the thermodynamic and dynamic behaviour of anisotropically interacting colloids by means of theoretical and numerical techniques. Colloidal suspensions, i.e. micro-- and nano--sized particles dispersed in a continuous phase, are a topic of great interest in several fields, including material science, soft matter and biophysics. Common in everyday life in the form of soap, milk, cream, etc., colloids have been used for decades as models for atomic and molecular systems, since both classes of systems share many features like critical phenomena, crystallisation and glass transition. Experimental investigation of colloidal systems is made easier by the large size of colloids, which makes it possible to employ visible light as an experimental probe to investigate these systems. Moreover, since the mass of the particles controls the timescales of the dynamics, relaxation times of colloidal suspensions, ranging from seconds to years, orders of magnitude larger than their atomic counterparts, are more easily experimentally accessible. By exploiting this intrinsic slowness, with respect to molecular liquids, present day experimental techniques make it possible to follow in time trajectories of ensembles of particles with tools like confocal microscopy, thus effectively allowing to reconstruct the whole phase space trajectory of the system. In addition, it is also possible to manipulate single and multiple objects using techniques like optical tweezers, magnetic tweezers and atomic force microscopy. With single-molecule force spectroscopy one can arrange particles in ordered structures or measure properties like stiffness or mechanical responses (as in pulling experiments on RNA and DNA strands of particles and aggregates). A remarkable difference between the molecular and the colloidal world is that in the former the interactions between the basic constituents are fixed by nature, while in the latter the effective potential between two particles can be controlled by accurately designing and synthesizing the building blocks or tuned by changing the properties of the solvent. In the last decade many new sophisticated techniques for particle synthesis have been developed and refined. These recent advances allow for the creation of an incredible variety of non-spherically, i.e. anisotropically, interacting building blocks. The anisotropy can arise from shape, surface patterning, form of the interactions or a combination thereof. Examples are colloidal cubes, Janus particles, triblock Janus particles, patchy particles, magnetic spheres and many others. The recent blossoming of experimental, theoretical and numerical studies and research on the role of the anisotropy has highlighted the richness of phenomena that these systems exhibit. Relevant examples for the present Thesis are valence-limited building blocks, i.e colloids with a maximum number of bound neighbours, and non-spherical particles with an aspect ratio, i.e. the ratio of the width of a particle to its height, significantly different from $1$. The simplest example of valence-limited colloids is given by the so-called \textit{patchy} particles: colloids decorated with attractive spots (patches) on the surface. If the width and the range of the patches are chosen in such a way that each patch can form no more than one bond, then the total number of bound first neighbours per particle $M$ can not exceed the number of patches. For particles interacting through short-ranged isotropic potentials, $M \approx 12$. It has been shown that changing the valence $M$ has dramatic effects, both qualitative and quantitative, on the dynamic and thermodynamic properties of such systems. At high densities patchy colloids can self-assemble into a large variety of crystal structures, depending on valence, geometry and external parameters. We will mostly focus on low-density systems. The second class of systems pertinent to the present work comprises anisotropically shaped particles that, depending on the aspect ratio and the values of the external parameters, can exhibit liquid crystal phases which may display orientational long-range order. Nematic, in which there is no translational order, smectic, in which particles are ordered in layers and thus exhibit translational order in one dimension, and columnar phases, in which particles self-assemble into cylindrical aggregates which can in turn become nematic or form two-dimensional lattices, do not exist in isotropic systems, since the anisotropy in shape is a prerequisite for the breaking of the orientational symmetry. Liquid crystals, discovered at the end of the 19th century have been thoroughly investigated for decades, leading to technological breakthroughs like LCD displays. Recently it has been suggested that liquid crystal phases occurring in dense solutions of short DNA double strands could have played a role in the prebiotic chemical generation of complementary H-bonded molecular assemblies. The main goal of the present Thesis is to study the structural, thermodynamic and, to a lesser extent, dynamic properties of systems interacting through anisotropic potentials at low densities and temperatures. In particular, we focus on the low-density phase behaviour of valence-limited systems. We use a variegated approach, comprising state-of-the-art Monte Carlo and Molecular Dynamics techniques and theoretical approaches, to analyse and shed some light on the effect of the anisotropy on the phase diagram and on the dynamics of such systems. As the effect of the valence on the phase diagram plays a major role in the models investigated throughout this Thesis, each Chapter is devoted to the study of the dynamics and thermodynamics of systems having a fixed or effective maximum valence $M$. In the last years a lot of effort has been devoted to the study of end-to-end stacking interactions between different strands of nucleic acids, which play an important role in both physical and biological applications of DNA and RNA. In Chapter~1, building on the experimental work of Bellini \textit{et al.}, we make use of a theoretical framework recently developed to tackle the problem of the isotropic--nematic phase coexistence in solutions of short DNA duplexes (DNADs). We compare the parameter-free theoretical predictions with results from large scale numerical simulations on GPUs of a coarse-grained realistic model and find a good quantitative agreement at low concentrations. We then predict the phase boundaries for different DNAD lengths and compare the results with experimental findings. In Chapter~2 we investigate the structural and thermodynamic properties of systems having $M=2$, that is systems that undergo an extensive formation of linear structures as temperature is lowered. We focus on bi-functional patchy particles whose interaction details are chosen to qualitatively mimic the behaviour of the low-density, low-temperature dipolar hard sphere (DHS) model by analysing the outcomes of the simulations carried out in Chapter~3. In particular, we are interested in the interplay between chains and rings in equilibrium polymerization processes in a region of the phase diagram where the formation of the latter is favoured. The very good quantitative agreement found by comparing numerical results with theoretical, parameter-free predictions calls for an extension of the theory with the inclusion of branching, in order to understand how the presence of rings affects the phase separation. Chapter~3 is devoted to the investigation of the phase behaviour of dipolar fluids, i.e. systems interacting mainly through dipole-dipole potentials. For spheres, the lowest-energy configuration is the nose-to-tail contact geometry, and hence the ground state is an infinite chain or ring like in regular $M=2$ systems. For finite temperatures, on the other hand, thermal fluctuations allow for the appearance of defects like dangling ends and chain branching which, in the language of this Thesis, makes for a temperature-dependent valence. This general mechanism, under some specific conditions, can lead to a very peculiar phase separation, driven by a balance between these \textit{topological} defects rather than by the energy/entropy competition usually responsible for regular gas--liquid phase transitions. This topological phase transition has been recently observed in a model system of patchy particles but it is unclear whether such mechanism still holds in dipolar fluids in general and in the DHS model in particular. We focus on the DHS model, whose phase behaviour at low densities and temperatures has been studied for decades but still remains largely unknown. In particular, we look for the gas--liquid critical point by means of state-of-the-art Monte Carlo simulations in a region where it has long been thought to be. We find no evidence of a phase transition and we speculate that this is due to an abundance of rings, providing a remarkable example of phase separation suppressed by self-assembly. In Chapter~4 we study the dynamics of tetravalent patchy particles in the optimal network density region. For this fixed value of density the system is able to form a fully connected random network, i.e. an ideal gel. Indeed, as the temperature is lowered, a percolating network forms and the dynamics slows down. Although the observed dynamical arrest is different from the glass case, where excluded volume interactions are dominant, the decay of the self-- and collective correlation functions of the resulting fluid bears similarities with that observed in glassy systems. Remarkably, comparing the characteristic decay times of density-density correlation functions with the average bond life, we find that only at very low $T$ the decay of the density fluctuations requires the breakage of bonds. In Chapter~5 we introduce DNA as a building block that can be used to rationally design novel, self-assembling materials with tunable properties. In this Chapter, we study the phase behaviour and the dynamics of four-armed DNA constructs at low densities. We use the coarse-grained, realistic DNA model employed in Chapter~1 and state-of-the-art simulation techniques, as presented in Chapter~6, to investigate systems composed of thousands of nucleotides undergoing a two-step self-assembling process and we quantitatively compare the outcome with experimental results obtained for a very similar system. In Chapter~6 we introduce Graphics Processing Units (GPUs) as valuable tools for present day numerical investigations. We outline both the architecture of NVIDIA GPUs and NVIDIA CUDA, the software layer built on top of the hardware required to program these devices. We then present the techniques employed to write an efficient, general Molecular Dynamics code and compare its performances with a regular CPU code. The observed performance boost allows us to tackle the analysis of the dynamics and thermodynamics of very large systems without having to resort to massive CPU clusters (see Chapters~1,~4 and~5). Our work shows that it is possible to predict the location of thermodynamic and dynamic \textit{locii} of very complicated objects by means of numerical simulations. Since the available computational power keeps increasing at a steady pace, it will be soon possible to repeat the pioneering study presented in this Thesis on a more automated basis and for even more complicated system. For example, it will be possible to directly study the isotropic--nematic phase transition of short DNA duplexes investigated in Chapter~1 or design self-assembling DNA strands able to reproduce the behaviour of the patchy colloids or dipolar fluids studied throughout this Thesis. Being able to carefully design the building blocks and then predict beforehand the properties of a compound will greatly simplify the process of synthesising tomorrow's materials

Formation of morphogen gradients

Morphogens are signaling molecules that play a key role in animal development. They spread from a restricted source into an adjacent target tissue forming a concentration gradient. The fate of cells in the target tissue is determined by the local concentration of such morphogens. Morphogen transport through the tissue has been studied in experiments which lead to the suggestion of several transport mechanisms. While diffusion in the extracellular space contributes to transport, recent experiments on the morphogen Decapentaplegic (Dpp) in the fruit fly Drosophila provide evidence for the importance of a cellular transport mechanism that was termed "planar transcytosis". In this mechanism, morphogens are transported through cells by repeated rounds of internalization and externalization. Starting from a microscopic theoretical description of these processes, we derive systems of nonlinear transport equations which describe the interplay of transcytosis and passive diffusion. We compare the results of numerical calculations based on this theoretical description of morphogen transport to recent experimental data on the morphogen Dpp in the Drosophila wing disk. Agreement with the experimental data is only achieved if the parameters entering the theoretical description are chosen such that transcytosis contributes strongly to transport. Analyzing the derived transport equations, we find that transcytosis leads to an increased robustness of the created gradients with respect to morphogen over-expression. Indications for this kind of robustness have been found in experiments. Furthermore, we theoretically investigate morphogen gradient formation in disordered systems. Here, an important question is how the position of concentration thresholds can be defined with high precision in the noisy environment present in typical developing tissues. Among other things, we find that the dimensionality of the system in which the gradient is formed plays an important role for the precision. Comparing gradients formed by transcytosis to those formed by extracellular diffusion, we find substantial differences that may result in a higher precision of gradients formed by transcytosis. Finally, we suggest several experiments to test the theoretical predictions of this work

Monte Carlo simulation studies of DNA hybridization and DNA-directed nanoparticle assembly

A coarse-grained lattice model of DNA oligonucleotides is proposed to investigate how fundamental thermodynamic processes are encoded by the nucleobase sequence at the microscopic level, and to elucidate the general mechanisms by which single-stranded oligonucleotides hybridize to their complements either in solution or when tethered to nanoparticles. Molecular simulations based on a high-coordination cubic lattice are performed using the Monte Carlo method. The dependence of the model's thermal stability on sequence complementarity is shown to be qualitatively consistent with experiment and statistical mechanical models. From the analysis of the statistical distribution of base-paired states and of the associated free-energy landscapes, two general hybridization scenarios are found. For sequences that do not follow a two-state process, hybridization is weakly cooperative and proceeds in multiple sequential steps involving stable intermediates with increasing number of paired bases. In contrast, sequences that conform to two-state thermodynamics exhibit moderately rough landscapes, in which multiple metastable intermediates appear over broad free-energy barriers. These intermediates correspond to duplex species that bridge the configurational and energetic gaps between duplex and denatured states with minimal loss of conformational entropy, and lead to a strongly cooperative hybridization. Remarkably, two-state thermodynamic signatures are generally observed in both scenarios. The role of cooperativity in the assembly of nanoparticles tethered with model DNA oligonucleotides is similarly addressed with the Monte Carlo method, where nanoparticles are represented as finely discretized hard-core spheres on a cubic lattice. The energetic and structural mechanisms of self-assembling are investigated by simulating the aggregation of small "satellite" particles from the bulk onto a large "core" particle. A remarkable enhancement of the system's thermal stability is attained by increasing the number of strands per satellite particle available to hybridize with those on the core particle. This cooperative process is driven by the formation of multiple bridging duplexes under favorable conditions of reduced translational entropy and the resultant energetic compensation; this behavior rapidly weakens above a certain threshold of linker strands per satellite particle. Cooperativity also enhances the structural organization of the assemblies by systematically narrowing the radial distribution of the satellite particles bound the core