Branching Boogaloo: Botanical Adventures in Multi-Mediated Morphologies

FormaLeaf is a software interface for exploring leaf morphology using parallel string rewriting grammars called L-systems. Scanned images of dicotyledonous angiosperm leaves removed from plants around Bard’s campus are displayed on the left and analyzed using the computer vision library OpenCV. Morphometrical information and terminological labels are reported in a side-panel. “Slider mode” allows the user to control the structural template and growth parameters of the generated L-system leaf displayed on the right. “Vision mode” shows the input and generated leaves as the computer ‘sees’ them. “Search mode” attempts to automatically produce a formally defined graphical representation of the input by evaluating the visual similarity of a generated pool of candidate leaves. The system seeks to derive a possible internal structural configuration for venation based purely off a visual analysis of external shape. The iterations of the generated L-system leaves when viewed in succession appear as a hypothetical development sequence. FormaLeaf was written in Processing

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations.Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrence matrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors.Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized.Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology

Effects of Surface Topography and Vibrations on Wetting: Superhydrophobicity, Icephobicity and Corrosion Resistance

Concrete and metallic materials are widely used in construction and water industry. The interaction of both these materials with water and ice (or snow) produces undesirable results and is therefore of interest. Water that gets absorbed into the pores of dry concrete expands on freezing and can lead to crack formation. Also, the ice accretion on concrete surfaces such as roadways can have disastrous consequence. Metallic components used in the water industry undergo corrosion due to contact with aqueous corrosive solutions. Therefore, it is desirable to make concrete water/ice-repellent, and to make metallic surfaces corrosion-resistant. Recent advances in micro/nanotechnology have made it possible to design functional micro/nanostructured surfaces with micro/nanotopography providing low adhesion. Some examples of such surfaces are superhydrophobic surfaces, which are extremely water repellent, and icephobic surfaces, which have low ice adhesion, repel incoming water droplets before freezing, or delay ice nucleation. This dissertation investigates the effects of surface micro/nanotopography and small amplitude fast vibrations on the wetting and adhesion of concrete with the goal of producing hydrophobic and icephobic concrete, and on the wetting of metallic surfaces to prevent corrosion. The relationship between surface micro/nanotopography and small fast vibrations is established using the method of separation of motions. Both these small scale effects can be substituted by an effective force or energy. The structure-property relationships in materials and surfaces are established. Both vibrations as well as surface micro/nanopatterns can affect wetting properties such as contact angle and surface free energy. Hydrophobic engineered cementitious composite samples are produced by controlling their surface topography and surface free energy. The surface topography is controlled by varying the concrete mixture composition. The surface free energy of concrete is lowered using a hydrophobic emulsion. The hydrophobic concrete samples were able to repel incoming water droplets as well as resist droplet pinning. Corrosion resistance is achieved in cast iron samples by rendering them superhydrophobic. The corrosion resistance of superhydrophobic surfaces with micro/nanotopography may be explained by the low effective contact area with the electrolyte. The experimental results matched the theoretical predictions based on surface roughness and wettability. The icephobicity of engineered cementitious composite samples is achieved by hydrophobization, by using coatings containing dielectric material (such as polyvinyl alcohol fibers), and by controlling the surface topography. Two aspects of the icephobicity of concrete, namely, the repulsion of incoming water droplets before freezing and the ice adhesion strength, are investigated experimentally. It is found that icephobic performance of concrete depends on these parameters – the hydrophobic emulsion concentration, the polyvinyl alcohol fiber content, the water to cement ratio, and the sand to cement ratio. The potential for biomimetic icephobicity of thermogenic skunk cabbage plant is investigated, and it is found that the surface topography of its leaves can affect the heat transfer from the plant to the surrounding snow. The hierarchical microstructure of the leaf surface coupled with its high adhesion to water suggests the presence of an impregnated wetting state, which can minimize the heat loss. Thus functional materials and surfaces, such as hydrophobic and icephobic engineered cementitious composites and corrosion resistant metallic surfaces, can be produced by controlling the surface micro/nanotopography

Morphogenesis of Class IV Neurons in Drosophila melanogaster

The establishment of the neuron\u27s morphology is essential to its function. The class IV neurons of the Drosophila melanogaster larva are two-dimensional sensory neurons that develop a complex dendritic arbor sensitive to mechanical stimuli. The fully-developed dendritic tree results from a multitude of stochastic processes including dendritic tip growth, branching and self-avoidance. However, it is yet unknown how the microscopic dendritic growth processes produce the macroscopic morphology of the class IV neurons. In this study, we aim to bridge this gap by formulating multi-scale models of neuronal dendritic morphogenesis. We begin by analyzing the tip dynamics and branching process of class IV dendritic trees. We find that the tip growth dynamics can be described by a Markov process that transitions between three velocity states: growing, paused and shrinking. Driven by the results of our analysis, we propose two types of model of morphogenesis. First, we use the mean-field approximation to formulate dendritic tree growth as a system of reaction-diffusion equations with two kinds of species, dendrites and tips. This coarse-grained approach predicts that the dendritic tree grows by the propagation of a density wave whose tail stabilizes to a steady-state. Second, we construct an agent-based model of morphogenesis that implements the stochastic rules of microscopic tip growth and branching whose combined effects lead to the development of the dendritic tree. Within the limitations of the model, this more fine-grained approach predicts morphometrics that agree with the measured values. In summary, our results characterize the development of class IV neurons and provide a framework to understand how the large-scale morphology of the class IV neuron dendritic tree emerges from the local stochastic growth of its branches

Modelling Electrostatic Interactions and Solvation in Chromatin: from the single nucleosome towards the chromatin fibre

Chromatin is a complex of proteins and DNA found in the nuclei of eukaryotic cells. It reinforces the DNA and its topology tunes DNA transcription and gene expression. It is formed by nucleosomes, structures composed of an octameric protein core and approximately 147 base pairs of DNA. Chromatin is an extremely complex system, the behaviour of which is ruled by both mechanical and electrostatic factors that are depend on its structure, and biomolecular interactions occurring in the cell nucleus. In this thesis, I analyse chromatin compaction from an electrostatic perspective and focus on the role of electrostatics and solvation as determinants of the topology of chromatin. I examine the effect of the histone tails and propose a methodology to connect electrostatic calculations to the structural and functional features of protein-DNA systems. This methodology can also be combined with coarse-grained representations. I study the electrostatic forces acting on the phosphate atoms of the DNA backbone. I investigate the electrostatic origins of effects such as different stages in DNA unwrapping, nucleosome destabilisation upon histone tail truncation, and the role of specific arginines and lysines undergoing Post - Translational Modifications. I find that the positioning of the histone tails can oppose the attractive pull of the histone core, locally deform the DNA, and tune DNA unwrapping. I conduct an analysis of the porosity of nucleosomes and related to the importance of solvation phenomena. I complement and support my computational findings on nucleosome electrostatic interactions experimental Zeta Potential and Dynamic Light Scattering measurements on single nucleosomes under varying ionic concentrations, providing information on the surface charge and the size of nucleosomes. I present a comprehensive study of the electrostatic interactions between nucleosome pairs sampling different translations and rotations. My analysis aims to provide a cohesive description of nucleosome electrostatic interactions in the chromatin fibre, combining information on the energetics of different relative positions of nucleosomes, especially in very tight packing situations. In addition to numerical estimates of electrostatic interaction energy of nucleosomes at different relative distances and orientations, obtained within the Poisson-Boltzmann framework, I present their approximation by analytical asymptotic expressions, where nucleosomes are approximated as monopoles and dipoles centred in dielectric spheres immersed in an electrolytic solution. I am able to identify a non-linearity region around the nucleosomes, and to exploit the fact that that in points outside that region the electrostatic potential can be described by the linearised Poisson-Boltzmann Equation

PHYSICAL CHEMISTRY OF COLLOIDAL-NANOPARTICLE INTERACTIONS

Following the advances in the design and characterization of engineered nanoparticles, biomaterials came into contact with the nano-world. Among many implementations of bio-nanotechnology, there is an increasing scientific and industrial interest in designing complex/hybrid structures (micro/macro) by merging the advantageous of inorganic colloidal particles (fixed shape, hard matter) with organic biopolymers (flexible shape, soft matter).In the current dissertation, nanostructured silica suspensions with tunable rheological characteristics were designed via steric and electrostatic interactions. Perturbation of short range interactions, protein bridging and silica re-dispersion were reported to play key roles in the macro-structure formation as determined by light scattering, steady state shear and small angle oscillatory shear rheology. Tunable rheology was attributed to the physiochemical interactions of disordered fractal microstructures that are formed via spontaneous, non-directional and random complexation. The thermodynamic nature of complexation was resolved by discriminating the free energy change into its enthalpic and entropic contributions through circular dichroism and isothermal titration calorimetry. The dominant entropic pathway of complexation, showed that the assembly of supra-colloid microstructures by using nano-particles and biopolymers as building blocks is not limited by unfavorable enthalpic restrictions

Design and Topology Optimisation of Tissue Scaffolds

Tissue restoration by tissue scaffolding is an emerging technique with many potential applications. While it is well-known that the structural properties of tissue scaffolds play a critical role in cell regrowth, it is usually unclear how optimal tissue regeneration can be achieved. This thesis hereby presents a computational investigation of tissue scaffold design and optimisation. This study proposes an isosurface-based characterisation and optimisation technique for the design of microscopic architecture, and a porosity-based approach for the design of macroscopic structure. The goal of this study is to physically define the optimal tissue scaffold construct, and to establish any link between cell viability and scaffold architecture. Single-objective and multi-objective topology optimisation was conducted at both microscopic and macroscopic scales to determine the ideal scaffold design. A high quality isosurface modelling technique was formulated and automated to define the microstructure in stereolithography format. Periodic structures with maximised permeability, and theoretically maximum diffusivity and bulk modulus were found using a modified level set method. Microstructures with specific effective diffusivity were also created by means of inverse homogenisation. Cell viability simulation was subsequently conducted to show that the optimised microstructures offered a more viable environment than those with random microstructure. The cell proliferation outcome in terms of cell number and survival rate was also improved through the optimisation of the macroscopic porosity profile. Additionally artificial vascular systems were created and optimised to enhance diffusive nutrient transport. The formation of vasculature in the optimisation process suggests that natural vascular systems acquire their fractal shapes through self-optimisation