One core aspect of immersive visualization labs is to develop and provide powerful tools and applications that allow for efficient analysis and exploration of scientific data. As the requirements for such applications are often diverse and complex, the same applies to the development process. This has led to a myriad of different tools, frameworks, and approaches that grew and developed over time. The steady advance of commercial off-the-shelf game engines such as Unreal Engine has made them a valuable option for development in immersive visualization labs. In this work, we share our experience of migrating to Unreal Engine as a primary developing environment for immersive visualization applications. We share our considerations on requirements, present use cases developed in our lab to communicate advantages and challenges experienced, discuss implications on our research and development environments, and aim to provide guidance for others within our community facing similar challenges.

We present haplotype-resolved reference genomes and comparative analyses of six ape species, namely: chimpanzee, bonobo, gorilla, Bornean orangutan, Sumatran orangutan, and siamang. We achieve chromosome-level contiguity with unparalleled sequence accuracy (<1 error in 500,000 base pairs), completely sequencing 215 gapless chromosomes telomere-to-telomere. We resolve challenging regions, such as the major histocompatibility complex and immunoglobulin loci, providing more in-depth evolutionary insights. Comparative analyses, including human, allow us to investigate the evolution and diversity of regions previously uncharacterized or incompletely studied without bias from mapping to the human reference. This includes newly minted gene families within lineage-specific segmental duplications, centromeric DNA, acrocentric chromosomes, and subterminal heterochromatin. This resource should serve as a definitive baseline for all future evolutionary studies of humans and our closest living ape relatives.

Biological systems inherently span multiple levels, which can pose challenges in spatial representation for modelers. We present a protocol that utilizes colored Petri nets to construct and analyze biological models of systems, encompassing both unilevel and multilevel scenarios. We detail a modeling workflow exploiting the PetriNuts platform comprising a set of tools linked together via common file formats. We describe steps for modeling preparation, component-level modeling and analysis, followed by system-level modeling and analysis, and model use.

Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems.

The design of optical lens assemblies is a difficult process that re-quires lots of expertise. The teaching of this process today is done on physical optical benches, which are often too expensive for students to purchase. One way of circumventing these costs is to use software to simulate the optical bench. This work presents a virtual optical bench, which leverages realtime ray tracing in combination with VR rendering to create a teaching tool which creates a repeatable, non-hazardous and feature-rich learning environment. The resulting application was evaluated in an expert review with 6 optical engineers.

We present a methodology for systematically extending epidemic models to multilevel and multiscale spatio-temporal pandemic ones. Our approach builds on the use of coloured stochastic and continuous Petri nets facilitating the sound component-based extension of basic SIR models to include population stratification and also spatio-geographic information and travel connections, represented as graphs, resulting in robust stratified pandemic metapopulation models. The epidemic components and the spatial and stratification data are combined together in these coloured models and built in to the underlying expanded models. As a consequence this method is inherently easy to use, producing scalable and reusable models with a high degree of clarity and accessibility which can be read either in a deterministic or stochastic paradigm. Our method is supported by a publicly available platform PetriNuts ; it enables the visual construction and editing of models; deterministic, stochastic and hybrid simulation as well as structural and behavioural analysis. All models are available as Supplementary Material , ensuring reproducibility. All uncoloured Petri nets can be animated within a web browser at https://www-dssz.informatik.tu-cottbus.de/DSSZ/Research/ModellingEpidemics , assisting the comprehension of those models. We aim to enable modellers and planners to construct clear and robust models by themselves.

This research project aimed to overcome the challenge of analysing human language relationships, facilitate the grouping of languages and formation of genealogical relationship between them by developing automated comparison techniques. Techniques were based on the phonetic representation of certain key words and concept. Example word sets included numbers 1-10 (curated), large database of numbers 1-10 and sheep counting numbers 1-10 (other sources), colours (curated), basic words (curated). To enable comparison within the sets the measure of Edit distance was calculated based on Levenshtein distance metric. This metric between two strings is the minimum number of single-character edits, operations including: insertions, deletions or substitutions. To explore which words exhibit more or less variation, which words are more preserved and examine how languages could be grouped based on linguistic distances within sets, several data analytics techniques were involved. Those included density evaluation, hierarchical clustering, silhouette, mean, standard deviation and Bhattacharya coefficient calculations. These techniques lead to the development of a workflow which was later implemented by combining Unix shell scripts, a developed R package and SWI Prolog. This proved to be computationally efficient and permitted the fast exploration of large language sets and their analysis.

The modelling of biological systems is accompanied with epistemic uncertainties that range from structural uncertainty to parametric uncertainty due to such limitations as insufficient understanding of the underlying mechanism and incomplete measurement data of a system. Fuzzy logic approaches such as fuzzy Petri nets (FPNs) are effective in addressing these issues. In this paper, we review FPNs that have been used for modelling uncertain biological systems, which we classify in three categories: basic fuzzy Petri nets, fuzzy quantitative Petri nets and Petri nets with fuzzy kinetic parameters. For each category of these FPNs, we summarize its modelling capabilities and current applications, discuss its merits and drawbacks and give suggestions for further research. This understanding on how to use FPNs for modelling uncertain biological systems will assist readers in selecting appropriate FPN classes for specific modelling circumstances. This review may also promote the extensive research and application of FPNs in the systems biology area.

BACKGROUND:Quorum sensing drives biofilm formation in bacteria in order to ensure that biofilm formation only occurs when colonies are of a sufficient size and density. This spatial behaviour is achieved by the broadcast communication of an autoinducer in a diffusion scenario. This is of interest, for example, when considering the role of gut microbiota in gut health. This behaviour occurs within the context of the four phases of bacterial growth, specifically in the exponential stage (phase 2) for autoinducer production and the stationary stage (phase 3) for biofilm formation. RESULTS:We have used coloured hybrid Petri nets to step-wise develop a flexible computational model for E.coli biofilm formation driven by Autoinducer 2 (AI-2) which is easy to configure for different notions of space. The model describes the essential components of gene transcription, signal transduction, extra and intra cellular transport, as well as the two-phase nature of the system. We build on a previously published non-spatial stochastic Petri net model of AI-2 production, keeping the assumptions of a limited nutritional environment, and our spatial hybrid Petri net model of biofilm formation, first presented at the NETTAB 2017 workshop. First we consider the two models separately without space, and then combined, and finally we add space. We describe in detail our step-wise model development and validation. Our simulation results support the expected behaviour that biofilm formation is increased in areas of higher bacterial colony size and density. Our analysis techniques include behaviour checking based on linear time temporal logic. CONCLUSIONS:The advantages of our modelling and analysis approach are the description of quorum sensing and associated biofilm formation over two phases of bacterial growth, taking into account bacterial spatial distribution using a flexible and easy to maintain computational model. All computational results are reproducible.

Friedreich's ataxia (FRDA) is a rare autosomal recessive inherited neurodegenerative disease which is the result of a triplet repeat expansion in the intronic region of the frataxin FXN gene resulting in depleted frataxin protein expression. Disease onset is usually in childhood and causes progressive damage to the nervous system resulting in progressive disability. This work uses computer aided classification techniques to identify which measures of the disease progression, including accurate determination of the shortest allele repeat length, are the most informative when trying to predict likely disease progression and prognosis. Further we investigate the possibility of a gender difference in the progression of the disease. Our results highlight the importance of accurate determination GAA repeat length in any clinical predictions showing that the number of repeats is the best prognostic tool in FRDA and is strongly linked to the age at onset disease. Further that there are possible gender dependent differences in cardiac measurements recorded from patients of similar age of onset and GAA repeat length.

We consider localised DNA computation, where a DNA strand walks along a binary decision graph to compute a binary function. One of the challenges for the design of reliable walker circuits consists in leakage transitions, which occur when a walker jumps into another branch of the decision graph. We automatically identify leakage transitions, which allows for a detailed qualitative and quantitative assessment of circuit designs, design comparison, and design optimisation. The ability to identify leakage transitions is an important step in the process of optimising DNA circuit layouts where the aim is to minimise the computational error inherent in a circuit while minimising the area of the circuit. Our 2D modelling approach of DNA walker circuits relies on coloured stochastic Petri nets which enable functionality, topology and dimensionality all to be integrated in one two-dimensional model. Our modelling and analysis approach can be easily extended to 3-dimensional walker systems.

Asparagine synthetase activity in cereals has become an important issue with the discovery that free asparagine concentration determines the potential for formation of acrylamide, a probably carcinogenic processing contaminant, in baked cereal products. Asparagine synthetase catalyses the ATP-dependent transfer of the amino group of glutamine to a molecule of aspartate to generate glutamate and asparagine. Here, asparagine synthetase-encoding polymerase chain reaction (PCR) products were amplified from wheat (Triticum aestivum) cv. Spark cDNA. The encoded proteins were assigned the names TaASN1, TaASN2, and TaASN3 on the basis of comparisons with other wheat and cereal asparagine synthetases. Although very similar to each other they differed slightly in size, with molecular masses of 65.49, 65.06, and 66.24 kDa, respectively. Chromosomal positions and scaffold references were established for TaASN1, TaASN2, and TaASN3, and a fourth, more recently identified gene, TaASN4. TaASN1, TaASN2, and TaASN4 were all found to be single copy genes, located on chromosomes 5, 3, and 4, respectively, of each genome (A, B, and D), although variety Chinese Spring lacked a TaASN2 gene in the B genome. Two copies of TaASN3 were found on chromosome 1 of each genome, and these were given the names TaASN3.1 and TaASN3.2. The TaASN1, TaASN2, and TaASN3 PCR products were heterologously expressed in Escherichia coli (TaASN4 was not investigated in this part of the study). Western blot analysis identified two monoclonal antibodies that recognized the three proteins, but did not distinguish between them, despite being raised to epitopes SKKPRMIEVAAP and GGSNKPGVMNTV in the variable C-terminal regions of the proteins. The heterologously expressed TaASN1 and TaASN2 proteins were found to be active asparagine synthetases, producing asparagine and glutamate from glutamine and aspartate. The asparagine synthetase reaction was modeled using SNOOPY® software and information from the BRENDA database to generate differential equations to describe the reaction stages, based on mass action kinetics. Experimental data from the reactions catalyzed by TaASN1 and TaASN2 were entered into the model using Copasi, enabling values to be determined for kinetic parameters. Both the reaction data and the modeling showed that the enzymes continued to produce glutamate even when the synthesis of asparagine had ceased due to a lack of aspartate.

BACKGROUND:Determining the value of kinetic constants for a metabolic system in the exact physiological conditions is an extremely hard task. However, this kind of information is of pivotal relevance to effectively simulate a biological phenomenon as complex as metabolism.RESULTS:To overcome this issue, we propose to investigate emerging properties of ensembles of sets of kinetic constants leading to the biological readout observed in different experimental conditions. To this aim, we exploit information retrievable from constraint-based analyses (i.e. metabolic flux distributions at steady state) with the goal to generate feasible values for kinetic constants exploiting the mass action law. The sets retrieved from the previous step will be used to parametrize a mechanistic model whose simulation will be performed to reconstruct the dynamics of the system (until reaching the metabolic steady state) for each experimental condition. Every parametrization that is in accordance with the expected metabolic phenotype is collected in an ensemble whose features are analyzed to determine the emergence of properties of a phenotype. In this work we apply the proposed approach to identify ensembles of kinetic parameters for five metabolic phenotypes of E. Coli, by analyzing five different experimental conditions associated with the ECC2comp model recently published by Hädicke and collaborators.CONCLUSIONS:Our results suggest that the parameter values of just few reactions are responsible for the emergence of a metabolic phenotype. Notably, in contrast with constraint-based approaches such as Flux Balance Analysis, the methodology used in this paper does not require to assume that metabolism is optimizing towards a specific goal.

A whole genome metabolic model (GEM) is essentially a reconstruction of a network of enzyme-enabled chemical reactions representing the metabolism of an organism, based on information present in its genome. Such models have been designed so that flux balance analysis (FBA) can be applied in order to analyse metabolism under steady state. For this purpose, a biomass function is added to these models as an overall indicator of the model’s viability. Our objective is to develop dynamic models based on these FBA models in order to observe new and complex behaviours, including transient behaviour. There is however a major challenge in that the biomass function does not operate under dynamic simulation. An appropriate biomass function would enable the estimation under dynamic simulation of the growth of both wild-type and genetically modified bacteria under different, possibly dynamically changing growth conditions. Using data analytics techniques, we have developed a dynamic biomass function which acts as a faithful proxy for the FBA equivalent for a reduced GEM for E. coli. This involved consolidating data for reaction rates and metabolite concentrations generated under dynamic simulation with gold standard target data for biomass obtained by steady state analysis using FBA. It also led to a number of interesting insights regarding biomass fluxes for pairs of conditions. These findings were reproduced in our dynamic proxy function.

Owing to the availability of data of one biological phenomenon at different levels/scales, modelling of biological systems is moving from single level/scale to multiple levels/scales, which introduces a number of challenges. Coloured Petri nets (ColPNs) have been successfully applied to multilevel, multiscale and multidimensional modelling of some biological systems, addressing many of these challenges. In this article, we first review the basics of ColPNs and some popular extensions, and then their applications for multilevel, multiscale and multidimensional modelling of biological systems. This understanding of how to use ColPNs for modelling biological systems will assist readers in selecting appropriate ColPN classes for specific modelling circumstances.

It is now becoming feasible to determine the composition of an individual gut microbiota (gut microflora), as well as the individual genome. In addition, whole genome scale metabolic models (GEMs) exist for a range of bacteria, and also for human. In principle this enables us to build models for gut microbiota by aggregating strain-specific models and also place this within the human context, and to make predictions on a personalised basis of the influence of gut microbiota on human metabolism, and how the interactions between these microbiota and also the human may evolve. Such aggregation, however, raises several challenges, which we discuss in this paper. Furthermore, we present techniques and supporting tools which permit the development of personlised models for human – gut microbiota interaction. The construction of such models is supported by a suite of modelling and analysis tools which permit the exploration of the dynamic behaviour of the very large metabolic models, comprising Snoopy, Charlie, Prolog, MC2, and Marcie. Our tools could be applied to populations of models in the context of human - gut microbiota in- teractions. Our approach that we have developed permits the description of the dynamic behavioural interaction between different bacterial strains and their human host on a personalised level within one aggregated model represented as a coloured Petri net. We use simulative model checking techniques over coloured traces to analyse the huge amounts of data generated by the dynamic simulation of these very large and hierarchically structured models.