Dear colleagues, We thank you for your comments on our manuscript [1] and for raising the important question of automation [2]. The publications by Sepp et al. (2019) [3] and Liu et al. (2024) [4] focus on one type of PBPK models, denominated ‘for biologics’. In these models, each compartment (tissue/organ) is divided into vascular, endothelial endosomal and interstitial (sub-)compartments, and lymphatic flows are also represented [3, 4]. In these examples, the pharmacokinetics of proteins are represented. Notably, the protein distribution within tissues includes processes of passive transport across two types of pores via diffusion or fluid convection, processes of pinocytosis, binding, recycling and degradation [4]. This type of PBPK models is complex and their authors successfully used specific tools for automated code generation. In particular, Liu et al. (2024) used ‘mathematical sets’ available in the Ubiquity package, which facilitates assembling model components in R-language [4]. Sepp et al. (2019) used the MATLAB script PBPKassembler.m for automated code generation, where files in Simbiology (MATLAB) and Excel formats were combined [3]. Briefly, automation can help build models, which is highly valuable notably for complex models. Having a robust, thoroughly validated platform for automated code generation will indeed contribute to accessibility and reproducibility. Also, automation of model building seems to be a natural way to address the case of large models, in which manual scripting will be prone to introducing errors. Smaller models or those with fewer equations do not necessarily require automation. From our experience, writing the script manually provides a deep understanding of the underlying model mechanics. Manually written scripts also allow to give a clear view of how equations and parameters are applied, which we consider especially valuable for teaching and knowledge transfer. To conclude, as observed in other niches of systems biology, automation brings significant benefits to the modelling field, e.g. there is a plethora of tools that reconstruct and benchmark genome-scale metabolic models. As the correspondence authors argue, there is more scope for automation in the current practices of PBPK modelling. To facilitate this, we have opted to begin the journey of standardization via open collaboration through ELIXIR. We are looking forward to continuing to engage the research community towards automatic construction, validation and deposition of PBPK models. Thank you. Best regards, Elena Domínguez-Romero, Stanislav Mazurenko, Martin Scheringer, Vítor Martins dos Santos, Chris Evelo, Mihail Anton, John M. Hancock, Anže Županič, and Maria Suarez-Diez. None declared.

Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields.

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. SHORT ABSTRACT: There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

SUMMARY: The vast, uncoordinated proliferation of bioinformatics resources (databases, software tools, training materials etc.) makes it difficult for users to find them. To facilitate their discovery, various services are being developed to collect such resources into registries. We have developed BioCIDER, which, rather like online shopping 'recommendations', provides a contextualization index to help identify biological resources relevant to the content of the sites in which it is embedded. AVAILABILITY AND IMPLEMENTATION: BioCIDER (www.biocider.org) is an open-source platform. Documentation is available online (https://goo.gl/Klc51G), and source code is freely available via GitHub (https://github.com/BioCIDER). The BioJS widget that enables websites to embed contextualization is available from the BioJS registry (http://biojs.io/). All code is released under an MIT licence. CONTACT: [email protected] or [email protected] or [email protected].

Abstract Motivation: The identification of nucleosomes along the chromatin is key to understanding their role in the regulation of gene expression and other DNA-related processes. However, current experimental methods (MNase-ChIP, MNase-Seq) sample nucleosome positions from a cell population and contain biases, making thus the precise identification of individual nucleosomes not straightforward. Recent works have only focused on the first point, where noise reduction approaches have been developed to identify nucleosome positions. Results: In this article, we propose a new approach, termed NucleoFinder, that addresses both the positional heterogeneity across cells and experimental biases by seeking nucleosomes consistently positioned in a cell population and showing a significant enrichment relative to a control sample. Despite the absence of validated dataset, we show that our approach (i) detects fewer false positives than two other nucleosome calling methods and (ii) identifies two important features of the nucleosome organization (the nucleosome spacing downstream of active promoters and the enrichment/depletion of GC/AT dinucleotides at the centre of in vitro nucleosomes) with equal or greater ability than the other two methods. Availability: The R code of NucleoFinder, an example datafile and instructions are available for download from https://sites.google.com/site/beckerjeremie/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Conventional phylogenetic analysis for characterizing the relatedness between taxa typically assumes that a single relationship exists between species at every site along the genome. This assumption fails to take into account recombination which is a fundamental process for generating diversity and can lead to spurious results. Recombination induces a localized phylogenetic structure which may vary along the genome. Here, we generalize a hidden Markov model (HMM) to infer changes in phylogeny along multiple sequence alignments while accounting for rate heterogeneity; the hidden states refer to the unobserved phylogenic topology underlying the relatedness at a genomic location. The dimensionality of the number of hidden states (topologies) and their structure are random (not known a priori) and are sampled using Markov chain Monte Carlo algorithms. The HMM structure allows us to analytically integrate out over all possible changepoints in topologies as well as all the unknown branch lengths. RESULTS: We demonstrate our approach on simulated data and also to the genome of a suspected HIV recombinant strain as well as to an investigation of recombination in the sequences of 15 laboratory mouse strains sequenced by Perlegen Sciences. Our findings indicate that our method allows us to distinguish between rate heterogeneity and variation in phylogeny caused by recombination without being restricted to 4-taxa data.

BACKGROUND: Large-scale international projects are underway to generate collections of knockout mouse mutants and subsequently to perform high throughput phenotype assessments, raising new challenges for computational researchers due to the complexity and scale of the phenotype data. Phenotypes can be described using ontologies in two differing methodologies. Traditionally an individual phenotypic character has either been defined using a single compound term, originating from a species-specific dedicated phenotype ontology, or alternatively by a combinatorial annotation, using concepts from a range of disparate ontologies, to define a phenotypic character as an entity with an associated quality (EQ). Both methods have their merits, which include the dedicated approach allowing use of community standard terminology, and the combinatorial approach facilitating cross-species phenotypic statement comparisons. Previously databases have favoured one approach over another. The EUMODIC project will generate large amounts of mouse phenotype data, generated as a result of the execution of a set of Standard Operating Procedures (SOPs) and will implement both ontological approaches to capture the phenotype data generated. RESULTS: For all SOPs a four-tier annotation is made: a high-level description of the SOP, to broadly define the type of data generated by the SOP; individual parameter annotation using the EQ model; annotation of the qualitative data generated for each mouse; and the annotation of mutant lines after statistical analysis. The qualitative assessments of phenodeviance are made at the point of data entry, using child PATO qualities to the parameter quality. To facilitate data querying by scientists more familiar with single compound terms to describe phenotypes, the mappings between the Mammalian Phenotype (MP) ontology and the EQ PATO model are exploited to allow querying via MP terms. CONCLUSION: Well-annotated and comparable phenotype databases can be achieved through the use of ontologically derived comparable phenotypic statements and have been implemented here by means of OBO compatible EQ annotations. The implementation we describe also sees scientists working seamlessly with ontologies through the assessment of qualitative phenotypes in terms of PATO qualities and the ability to query the database using community-accepted compound MP terms. This work represents the first time the combinatorial and single-dedicated approaches have both been implemented to annotate a phenotypic dataset.

Following the technological advances that have enabled genome-wide analysis in most model organisms over the last ten years, there has been unprecedented growth in genome and post-genomic sciences with concomitant generation of an exponentially increasing volume of data. As a result numerous resources have been created to store and archive the data and biological materials produced, which are of substantial value to the whole community. Sustained access facilitating re-use of this primary data is vital, not only for validation, but for re-analysis, testing of new hypotheses and developing new technologies/platforms. A common challenge for most data resources and biological repositories today is finding financial support for maintenance and development so as to best serve the scientific community. In this manuscript we examine the problems that currently confront the data and resource infrastructure underlying the biomedical sciences. We discuss the financial sustainability issues and potential business models that could be adopted by biological resources and consider long term data preservation issues within the context of mouse functional genomics efforts in Europe.

In recent years the European Commission has funded an increasing number of functional genomics projects aimed at using the mouse as a model of human disease. Many of these projects are producing large data volumes. A recently funded programme, CASIMIR (coordination and sustainability of international mouse informatics resources) aims to make recommendations on the most efficient way to integrate these datasets. In summer 2007 CASIMIR carried out a questionnaire survey of relevant EC-funded projects to determine their current use of data integration technologies and standards. This report describes the consortiumpsilas aims, its achievements so far, the results of the survey and initial conclusions deriving from it.

The Mouse Resource Browser MRB (http://bioit.fleming.gr/mrb) is an easy-to-use database for searching and retrieving mouse resource information. Currently, MRB hosts a list of 200 mouse resources which are divided in 33 different categories. Apart from core information such as URL(s), contact information and free text descriptions of domain content, MRB holds valuable technical information for each resource; server technology used, relational database management system(s) utilized, programming language(s) of implementation, schema descriptive documents or actual database dumps and most importantly information about the integration and interoperability services provided. MRB also hosts an index of ontologies - both OBO and non-OBO - and minimum information standards for biological investigations. Moreover, MRB has implemented the maturity scheme developed by the CASIMIR consortium and attempts to assess resources accordingly. Ultimately MRBpsilas goal is to provide a complete registry of mouse resources and their programmatic accessibility methods.

The torrent of data emerging from the application of new technologies to functional genomics and systems biology can no longer be contained within the traditional modes of data sharing and publication with the consequence that data is being deposited in, distributed across and disseminated through an increasing number of databases. The resulting fragmentation poses serious problems for the model organism community which increasingly rely on data mining and computational approaches that require gathering of data from a range of sources. In the light of these problems, the European Commission has funded a coordination action, CASIMIR (coordination and sustainability of international mouse informatics resources), with a remit to assess the technical and social aspects of database interoperability that currently prevent the full realization of the potential of data integration in mouse functional genomics. In this article, we assess the current problems with interoperability, with particular reference to mouse functional genomics, and critically review the technologies that can be deployed to overcome them. We describe a typical use-case where an investigator wishes to gather data on variation, genomic context and metabolic pathway involvement for genes discovered in a genome-wide screen. We go on to develop an automated approach involving an in silico experimental workflow tool, Taverna, using web services, BioMart and MOLGENIS technologies for data retrieval. Finally, we focus on the current impediments to adopting such an approach in a wider context, and strategies to overcome them.

MOTIVATION: A major challenge in modern biology is to link genome sequence information to organismal function. In many organisms this is being done by characterizing phenotypes resulting from mutations. Efficiently expressing phenotypic information requires combinatorial use of ontologies. However tools are not currently available to visualize combinations of ontologies. Here we describe CRAVE (Concept Relation Assay Value Explorer), a package allowing storage, active updating and visualization of multiple ontologies. RESULTS: CRAVE is a web-accessible JAVA application that accesses an underlying MySQL database of ontologies via a JAVA persistent middleware layer (Chameleon). This maps the database tables into discrete JAVA classes and creates memory resident, interlinked objects corresponding to the ontology data. These JAVA objects are accessed via calls through the middleware's application programming interface. CRAVE allows simultaneous display and linking of multiple ontologies and searching using Boolean and advanced searches.

UNLABELLED: Standardized phenotyping protocols are essential for the characterization of phenotypes so that results are comparable between different laboratories and phenotypic data can be related to ontological descriptions in an automated manner. We describe a web-based resource for the visualization, searching and downloading of standard operating procedures and other documents, the European Mouse Phenotyping Resource for Standardized Screens-EMPReSS. AVAILABILITY: Direct access: http://www.empress.har.mrc.ac.uk CONTACT: [email protected].

MOTIVATION: Low-complexity or cryptically simple sequences are widespread in protein sequences but their evolution and function are poorly understood. To date methods for the detection of low complexity in proteins have been directed towards the filtering of such regions prior to sequence homology searches but not to the analysis of the regions per se. However, many of these regions are encoded by non-repetitive DNA sequences and may therefore result from selection acting on protein structure and/or function. RESULTS: We have developed a new tool, based on the SIMPLE algorithm, that facilitates the quantification of the amount of simple sequence in proteins and determines the type of short motifs that show clustering above a certain threshold. By modifying the sensitivity of the program simple sequence content can be studied at various levels, from highly organised tandem structures to complex combinations of repeats. We compare the relative amount of simplicity in different functional groups of yeast proteins and determine the level of clustering of the different amino acids in these proteins. AVAILABILITY: The program is available on request or online at http://www.biochem.ucl.ac.uk/bsm/SIMPLE.

SIMPLE34 is an improved and enhanced version of SIMPLE for Vax and SunOS systems. It now provides a length-independent measure of the overall level of tri- and tetranucleotide motif clustering within nucleotide sequences and its significant deviation from random expectation. It now also provides information on tri- and tetranucleotide motifs showing higher levels of clustering than would be expected in random sequences. Sequence simplicity of test sequences can be judged with respect to random sequences generated on the basis of base composition, positional base composition or doublet frequency. These options can be used to investigate factors resulting in sequence simplicity.