Structural and congenital heart disease interventions: the role of three-dimensional printing

Advances in catheter-based interventions in structural and congenital heart disease have mandated an increased demand for three-dimensional (3D) visualisation of complex cardiac anatomy. Despite progress in 3D imaging modalities, the pre- and periprocedural visualisation of spatial anatomy is relegated to two-dimensional flat screen representations. 3D printing is an evolving technology based on the concept of additive manufacturing, where computerised digital surface renders are converted into physical models. Printed models replicate complex structures in tangible forms that cardiovascular physicians and surgeons can use for education, preprocedural planning and device testing. In this review we discuss the different steps of the 3D printing process, which include image acquisition, segmentation, printing methods and materials. We also examine the expanded applications of 3D printing in the catheter-based treatment of adult patients with structural and congenital heart disease while highlighting the current limitations of this technology in terms of segmentation, model accuracy and dynamic capabilities. Furthermore, we provide information on the resources needed to establish a hospital-based 3D printing laboratory.     

Raman ChemLighter: Fiber optic Raman probe imaging in combination with augmented chemical reality

Raman spectroscopy using fiber optic probe combines non‐contacted and label‐free molecular fingerprinting with high mechanical flexibility for biomedical, clinical and industrial applications. Inherently, fiber optic Raman probes provide information from a single point only, and the acquisition of images is not straightforward. For many applications, it is highly crucial to determine the molecular distribution and provide imaging information of the sample. Here, we propose an approach for Raman imaging using a handheld fiber optic probe, which is built around computer vision–based assessment of positional information and simultaneous acquisition of spectroscopic information. By combining this implementation with real‐time data processing and analysis, it is possible to create not only fiber‐based Raman imaging but also an augmented chemical reality image of the molecular distribution of the sample surface in real‐time. We experimentally demonstrated that using our approach, it is possible to determine and to distinguish borders of different bimolecular compounds in a short time. Because the method can be transferred to other optical probes and other spectroscopic techniques, it is expected that the implementation will have a large impact for clinical, biomedical and industrial applications.      

Molecular Visualization on the Holodeck

Can virtual reality be useful for visualizing and analyzing molecular structures and three-dimensional (3D) microscopy? Uses we are exploring include studies of drug binding to proteins and the effects of mutations, building accurate atomic models in electron microscopy and x-ray density maps, understanding how immune system cells move using 3D light microscopy, and teaching schoolchildren about biomolecules that are the machinery of life. Virtual reality (VR) offers immersive display with a wide field of view and head tracking for better perception of molecular architectures and uses 6-degree-of-freedom hand controllers for simple manipulation of 3D data. Conventional computer displays with trackpad, mouse and keyboard excel at two-dimensional tasks such as writing and studying research literature, uses for which VR technology is at present far inferior. Adding VR to the conventional computing environment could improve 3D capabilities if new user-interface problems can be solved. We have developed three VR applications: ChimeraX for analyzing molecular structures and electron and light microscopy data, AltPDB for collaborative discussions around atomic models, and Molecular Zoo for teaching young students characteristics of biomolecules. Investigations over three decades have produced an extensive literature evaluating the potential of VR in research and education. Consumer VR headsets are now affordable to researchers and educators, allowing direct tests of whether the technology is valuable in these areas. We survey here advantages and disadvantages of VR for molecular biology in the context of affordable and dramatically more powerful VR and graphics hardware than has been available in the past.     

Why model malaria?

The past 30 years have seen little tangible progress in alleviating the worldwide burden of malaria. Ellis McKenzie here discusses some of the history, problems and prospects of mathematical models of malaria, and the contributions that models might make towards progress. He argues that models can be powerful tools for integrating information from different disciplines, and that advances in computer modeling can complement and extend classic approaches.     

Using surface envelopes to constrain molecular modeling

Molecular density information (as measured by electron microscopic reconstructions or crystallographic density maps) can be a powerful source of information for molecular modeling. Molecular density constrains models by specifying where atoms should and should not be. Low-resolution density information can often be obtained relatively quickly, and there is a need for methods that use it effectively. We have previously described a method for scoring molecular models with surface envelopes to discriminate between plausible and implausible fits. We showed that we could successfully filter out models with the wrong shape based on this discrimination power. Ideally, however, surface information should be used during the modeling process to constrain the conformations that are sampled. In this paper, we describe an extension of our method for using shape information during computational modeling. We use the envelope scoring metric as part of an objective function in a global optimization that also optimizes distances and angles while avoiding collisions. We systematically tested surface representations of proteins (using all nonhydrogen heavy atoms) with different abundance of distance information and showed that the root mean square deviation (RMSD) of models built with envelope information is consistently improved, particularly in data sets with relatively small sets of short-range distances.     

4D bioinformatics: a new look at the ribosome as an example

We have adapted the Java Molecular Viewer (JMV) to virtual reality display environments, through a number of extensions to the Java 3D code. Phylogenetic information derived from multiple alignments (temporal information) can be overlaid onto molecule structures (spatial information). The number of sequences included in the underlying multiple alignment can be changed instantaneously, resulting in dynamical updates of the displayed information. JMV was also extended to handle an infinite number of objects (molecules) in the same display. The objects can be manipulated in six degrees of freedom simultaneously or independently. We have used the small subunit ribosomal RNA to demonstrate the system (http:// cave.ucalgary.ca), which can be used for any molecule with a resolved structure.     

Bio-Logic Builder: A Non-Technical Tool for Building Dynamical,Qualitative Models

Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized “bio-logic” modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool.     

Interaction-site prediction for protein complexes: a critical assessment

MOTIVATION: Proteins function through interactions with other proteins and biomolecules. Protein-protein interfaces hold key information toward molecular understanding of protein function. In the past few years, there have been intensive efforts in developing methods for predicting protein interface residues. A review that presents the current status of interface prediction and an overview of its applications and project future developments is in order. SUMMARY: Interface prediction methods rely on a wide range of sequence, structural and physical attributes that distinguish interface residues from non-interface surface residues. The input data are manipulated into either a numerical value or a probability representing the potential for a residue to be inside a protein interface. Predictions are now satisfactory for complex-forming proteins that are well represented in the Protein Data Bank, but less so for under-represented ones. Future developments will be directed at tackling problems such as building structural models for multi-component structural complexes.     

Internal representations for associative memory

We describe a class of feed forward neural network models for associative content addressable memory (ACAM) which utilize sparse internal representations for stored data. In addition to the input and output layers, our networks incorporate an intermediate processing layer which serves to label each stored memory and to perform error correction and association. We study two classes of internal label representations: the unary representation and various sparse, distributed representations. Finally, we consider storage of sparse data and sparsification of data. These models are found to have advantages in terms of storage capacity, hardware efficiency, and recall reliability when compared to the Hopfield model, and to possess analogies to both biological neural networks and standard digital computer memories.     

Modelling cartilage mechanobiology

The growth, maintenance and ossification of cartilage are fundamental to skeletal development and are regulated throughout life by the mechanical cues that are imposed by physical activities. Finite element computer analyses have been used to study the role of local tissue mechanics on endochondral ossification patterns, skeletal morphology and articular cartilage thickness distributions. Using single-phase continuum material representations of cartilage, the results have indicated that local intermittent hydrostatic pressure promotes cartilage maintenance. Cyclic tensile strains (or shear), however, promote cartilage growth and ossification. Because single-phase material models cannot capture fluid exudation in articular cartilage, poroelastic (or biphasic) solid/fluid models are often implemented to study joint mechanics. In the middle and deep layers of articular cartilage where poroelastic analyses predict little fluid exudation, the cartilage phenotype is maintained by cyclic fluid pressure (consistent with the single-phase theory). In superficial articular layers the chondrocytes are exposed to tangential tensile strain in addition to the high fluid pressure. Furthermore, there is fluid exudation and matrix consolidation, leading to cell 'flattening'. As a result, the superficial layer assumes an altered, more fibrous phenotype. These computer model predictions of cartilage mechanobiology are consistent with results of in vitro cell and tissue and molecular biology experiments.     

Quantifying the Effect of Polymer Blending through Molecular Modelling of Cyanurate Polymers

Modification of polymer properties by blending is a common practice in the polymer industry. We report here a study of blends of cyanurate polymers by molecular modelling that shows that the final experimentally determined properties can be predicted from first principles modelling to a good degree of accuracy. There is always a compromise between simulation length, accuracy and speed of prediction. A comparison of simulation times shows that 125ps of molecular dynamics simulation at each temperature provides the optimum compromise for models of this size with current technology. This study opens up the possibility of computer aided design of polymer blends with desired physical and mechanical properties.     

Information-Theoretic Analysis of the Dynamics of an Executable Biological Model

To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.     

BIOCOMPUTATION: Some history and prospects

At first glance, biology and computer science are diametrically opposed sciences. Biology deals with carbon based life forms shaped by evolution and natural selection. Computer Science deals with electronic machines designed by engineers and guided by mathematical algorithms. In this brief paper, we review biologically inspired computing. We discuss several models of computation which have arisen from various biological studies. We show what these have in common, and conjecture how biology can still suggest answers and models for the next generation of computing problems. We discuss computation and argue that these biologically inspired models do not extend the theoretical limits on computation. We suggest that, in practice, biological models may give more succinct representations of various problems, and we mention a few cases in which biological models have proved useful. We also discuss the reciprocal impact of computer science on biology and cite a few significant contributions to biological science.     

Graphical representations of the class I MHC cleft

We describe computer graphics and computer aided manufacture of three-dimensional models designed specifically to elucidate the cleft in the class I human leukocyte antigen. The models evolve from computer graphical representations and provide a geometrically and chemically concise and detailed view of the antigen binding site. The techniques provide a new approach to representations of binding sites. The model provides sufficient detail to support binding specificities analysis of active sites involved in protein and DNA binding.     

Interfacing a flow cytometer computer with a PC-based patient information system

We have established an interface between our flow cytometer's computer and the personal computer (PC) which supports our patient database system. The PC has been equipped with a commercially available IEEE-488 bus interface board which is connected to the interface bus of the cytometer's Hewlett-Packard 9000/300 computer (HP). The PC is set as a bus device with the same address as that of the HP's printer. It is programmed to examine the stream of data sent to the printer and extract from it and store in an MS-DOS text file selected information which subsequently may be transferred to the database system.     

Mobile learning applications to improve invertebrate zoology online teaching

The use of new technologies including personal mobile devices has become an indispensable tool in our daily lives, and thus its presence in education is becoming ever more ubiquitous. In the current scenario imposed by the COVID‐19 pandemic, in which in‐person presence in classrooms has been enormously reduced at all educational levels, the use of mobile learning and cutting‐edge methods can greatly improve the way students learn and enhance their online‐learning experience. Mobile applications, combined with extended reality technologies such as virtual reality (VR) and augmented reality (AR), are powerful tools that connect real and virtual environments and allow higher interaction for the user. We have leveraged the advantages of mobile learning and extended reality technologies to develop a series of mobile applications and associated educational activities for university‐level courses involving invertebrate zoology field work. In particular, we have developed (a) a VR SCUBA diving video to explore the diversity of a marine protected area; (b) an AR mobile app to visualize 3D models of marine invertebrates; and (c) a mobile‐based catalogue to explore the terrestrial biodiversity of one of the most diverse regions of Spain. Here we provide detailed information describing the design and creation of these tools, as well as their application in class, to facilitate and encourage their use in higher education. Despite the relatively recent application of these technologies in education, they have an enormous potential: they improve student motivation and learning, can be adapted to different learning styles, reduce social inequalities, and facilitate inclusiveness and diversity practices in the classroom.     

From passive diffusion to active cellular migration in mathematical models of tumour invasion

Mathematical models of tumour invasion appear as interesting tools for connecting the information extracted from medical imaging techniques and the large amount of data collected at the cellular and molecular levels. Most of the recent studies have used stochastic models of cell translocation for the comparison of computer simulations with histological solid tumour sections in order to discriminate and characterise expansive growth and active cell movements during host tissue invasion. This paper describes how a deterministic approach based on reaction-diffusion models and their generalisation in the mechano-chemical framework developed in the study of biological morphogenesis can be an alternative for analysing tumour morphological patterns. We support these considerations by reviewing two studies. In the first example, successful comparison of simulated brain tumour growth with a time sequence of computerised tomography (CT) scans leads to a quantification of the clinical parameters describing the invasion process and the therapy. The second example considers minimal hypotheses relating cell motility and cell traction forces. Using this model, we can simulate the bifurcation from an homogeneous distribution of cells at the tumour surface toward a nonhomogeneous density pattern which could characterise a pre-invasive stage at the tumour-host tissue interface.     

Structural systems biology: modelling protein interactions

Much of systems biology aims to predict the behaviour of biological systems on the basis of the set of molecules involved. Understanding the interactions between these molecules is therefore crucial to such efforts. Although many thousands of interactions are known, precise molecular details are available for only a tiny fraction of them. The difficulties that are involved in experimentally determining atomic structures for interacting proteins make predictive methods essential for progress. Structural details can ultimately turn abstract system representations into models that more accurately reflect biological reality.