Computational Biology Modeling Research Articles

Combination chemotherapy versus temozolomide for patients with methylated MGMT (m-MGMT) glioblastoma: results of computational biological modeling to predict the magnitude of treatment benefit

BackgroundA randomized trial in glioblastoma patients with methylated-MGMT (m-MGMT) found an improvement in median survival of 16.7 months for combination therapy with temozolomide (TMZ) and lomustine, however the approach remains controversial and relatively under-utilized. Therefore, we sought to determine whether comprehensive genomic analysis can predict which patients would derive large, intermediate, or negligible benefits from the combination compared to single agent chemotherapy.MethodsComprehensive genomic information from 274 newly diagnosed patients with methylated-MGMT glioblastoma (GBM) was downloaded from TCGA. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotypes representing hallmark behavior of cancers. In silico responses to TMZ, lomustine, and combination treatment were biosimulated. Efficacy scores representing the effect of treatment for each treatment strategy were generated and compared to each other to ascertain the differential benefit in drug response.ResultsDifferential benefits for each drug were identified, including strong, modest-intermediate, negligible, and deleterious (harmful) effects for subgroups of patients. Similarly, the benefits of combination therapy ranged from synergy, little or negligible benefit, and deleterious effects compared to single agent approaches.ConclusionsThe benefit of combination chemotherapy is predicted to vary widely in the population. Biosimulation appears to be a useful tool to address the disease heterogeneity, drug response, and the relevance of particular clinical trials observations to individual patients. Biosimulation has potential to spare some patients the experience of over-treatment while identifying patients uniquely situated to benefit from combination treatment. Validation of this new artificial intelligence tool is needed.

Hierarchical semantic composition of biosimulation models using bond graphs.

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.

VisuaLife: library for interactive visualization in rich web applications.

MotivationVisualization is a powerful tool to analyze, understand and present big data. Computational biology, bioinformatics and molecular modeling require dedicated tools, tailored to very complex, highly multidimensional data. Over the recent years, numerous tools have been developed for online presentation, but new challenges like the COVID-19 pandemic require new libraries which will guarantee fast development of online tools for a better understanding of biomedical data/results.ResultsVisuaLife is a Python library that provides a new approach to visualization in a web browser. It offers 2D and 3D plotting capabilities as well as widgets designed to display the most common biological data types: nucleotide or protein sequences, 3D biomolecular structures and multiple sequence alignments. Components provided by the VisuaLife library can be assembled into a web application to create an analysis tool tailored to provide multidimensional analysis of a specific research problem. VisuaLife, to our best knowledge, is the most modern solution that allows one to implement such a client-side interactivity in Python.Availability and implementationThe git repository of the library is hosted at BitBucket: https://bitbucket.org/dgront/visualife/. PyPI distribution is also provided for MacOS and Linux. While basic examples are provided in the supporting materials, the full documentation is available at ReadTheDocs website: https://visualife.readthedocs.io/.Supplementary information Supplementary data are available at Bioinformatics online.

To Sobol or not to Sobol? The effects of sampling schemes in systems biology applications

Computational and mathematical models in biology rely heavily on the parameters that characterize them. However, robust estimates for their values are typically elusive and thus a large parameter space becomes necessary for model study, particularly to make translationally impactful predictions. Sampling schemes exploring parameter spaces for models are used for a variety of purposes in systems biology, including model calibration and sensitivity analysis. Typically, random sampling is used; however, when models have a high number of unknown parameters or the models are highly complex, computational cost becomes an important factor. This issue can be reduced through the use of efficient sampling schemes such as Latin hypercube sampling (LHS) and Sobol sequences. In this work, we compare and contrast three sampling schemes – random sampling, LHS, and Sobol sequences – for the purposes of performing both parameter sensitivity analysis and model calibration. In addition, we apply these analyses to different types of computational and mathematical models of varying complexity: a simple ODE model, a complex ODE model, and an agent-based model. In general, the sampling scheme had little effect when used for calibration efforts, but when applied to sensitivity analyses, Sobol sequences exhibited faster convergence. While the observed benefit to convergence is relatively small, Sobol sequences are computationally less expensive to compute than LHS samples and also have the benefit of being deterministic, which allows for better reproducibility of results.

Mathematical Optimization Techniques in Computational of Biological Models

The aim of this research is focused on the mathematical optimization in computational systems biological models based on find the good method that gives converges faster. Our strategy is to use Bundle Method instead of using Subgradient Methods. Also, we improve the convergence of theoretical properties of the methods. The basic idea of approximation is the subdifferential of the objective function by using subgradients from previous iterations of a bundle method.

In silico trials predict that combination strategies for enhancing vesicular stomatitis oncolytic virus are determined by tumor aggressivity

BackgroundImmunotherapies, driven by immune-mediated antitumorigenicity, offer the potential for significant improvements to the treatment of multiple cancer types. Identifying therapeutic strategies that bolster antitumor immunity while limiting immune suppression is...

Targeting chromosome 12q amplification in relapsed glioblastoma: the use of computational biological modeling to identify effective therapy—a case report

Michael P. Castro, Negar Khanlou, Aria Fallah, Anusha Pampana, Aftab Alam, Deepak Anil Lala, Kunal Ghosh Ghosh Roy, Anish Raju R. Amara, Annapoorna Prakash, Divya Singh, Liptimayee Behura, Ansu Kumar, Shweta Kapoor

On approximate solutions for a fractional prey\u2013predator model involving the Atangana\u2013Baleanu derivative

Mathematical modeling has always been one of the most potent tools in predicting the behavior of dynamic systems in biology. In this regard, we aim to study a three-species prey–predator model in the context of fractional operator. The model includes two competing species with logistic growing. It is considered that one of the competitors is being predated by the third group with Holling type II functional response. Moreover, one another competitor is in a commensal relationship with the third category acting as its host. In this model, the Atangana–Baleanu fractional derivative is used to describe the rate of evolution of functions in the model. Using a creative numerical trick, an iterative method for determining the numerical solution of fractional systems has been developed. This method provides an implicit form for determining solution approximations that can be solved by standard methods in solving nonlinear systems such as Newton’s method. Using this numerical technique, approximate answers for this system are provided, assuming several categories of possible choices for the model parameters. In the continuation of the simulations, the sensitivity analysis of the solutions to some parameters is examined. Some other theoretical features related to the model, such as expressing the necessary conditions on the stability of equilibrium points as well as the existence and uniqueness of solutions, are also examined in this article. It is found that utilizing the concept of fractional derivative order the flexibility of the model in justifying different situations for the system has increased. The use of fractional operators in the study of other models in computational biology is recommended.

Predicting Resistance to the Combination of ATO and ATRA in APL Patients with PML-Rara Fusions, Using a Computational Biology Modeling Approach: Mycare-021-01

Background: Acute promyelocytic leukemia (APL) is a biologically and clinically distinct subtype of acute myeloid leukemia (AML) with unique molecular pathogenesis, clinical manifestations, and treatment. APL is cytogenetically characterized by a balanced translocation t(15;17) (q24;q21), which involves the retinoic acid receptor alpha (RARA) gene on chromosome 17 and the promyelocytic leukemia (PML) gene on chromosome 15 that results in a PML-RARA fusion gene (PMID: 30575821). The PML-RARA fusion gene is the most critical event involved in the pathogenesis of APL, reported in 99% of APL patients (PMID: 32182684). The fusion confers a selective sensitivity to the targeted drugs, arsenic trioxide (ATO) and all-trans-retinoic acid (ATRA), with response rates over 90% (PMID: 31635329). However, the mechanism of resistance in the minority of non-responders is not well understood. This study used the Cellworks Omics Biology Model (CBM) to predict response to the combination of ATO-ATRA in patients harboring the PML-RARA fusion and identify mechanisms of resistance. Methods: Outcomes of 30 APL patients treated with ATRA or ATRA plus ATO were compared with outcomes predicted by CBM (Table 1). Genomic data from 6 publications (Table 2) derived from whole exome sequencing (WES), targeted next-generation sequencing (NGS), copy number variation (CNV) and/or karyotype data were used. All data was anonymized, de-identified and exempt from IRB review. The available genomic data for each profile was entered into the CBM which generates a patient-specific disease protein network model using PubMed and other online resources. The CBM predicts the patient-specific biomarker and phenotype response of a personalized diseased cell to drug agents, radiation and cell signaling. Disease biomarkers that are unique to each patient were identified within the protein network models. ATO and ATRA were simulated on all 30 patient cases. The treatment impact was assessed by quantitatively measuring the drug's effect on a cell growth score which is a composite of the quantified values for cell proliferation, survival, and apoptosis, along with the simulated impact on each patient-specific disease biomarker score. Each patient-specific model was also digitally screened to identify response to ATO and ATRA. Results: The CBM correctly predicted the response to ATO and ATRA in 28 of 30 cases. The overall prediction accuracy was 93% with a PPV of 100%, NPV of 60%, sensitivity of 93%, and specificity of 100%. In 2 of 30 patients who did not respond to ATO and ATRA, the CBM identified clinically relevant deletions to EZH2, KMT2E, and HIPK2 genes. All three genes are located on chromosome 7 and these non-responders had monosomy 7. Conclusions: The Cellworks Omics Biology Model predicted response to ATO and ATRA in APL patients harboring PML-RARA fusions. Predicting non-response to ATO and ATRA in patients with PML-RARA fusion up-front could prevent ineffective treatment, avoid unnecessary adverse events and reduce treatment costs. Additionally, computational modeling can identify new mechanisms of resistance and suggest alternative regimens for non-responding patients by targeting the patient-specific disease biomarkers unique to each. Disclosures Howard: Servier: Consultancy, Other: Speaker; Boston Scientific: Consultancy; Sanofi: Consultancy, Other: Speaker; EUSA Pharma: Consultancy; Cellworks: Consultancy. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Tyagi:Cellworks Research India Private Limited: Current Employment. Kumari:Cellworks Research India Private Limited: Current Employment. Prasad:Cellworks Research India Private Limited: Current Employment. Mitra:Cellworks Research India Private Limited: Current Employment. Lala:Cellworks Research India Private Limited: Current Employment. Azam:Cellworks Research India Private Limited: Current Employment. Gupta:Cellworks Research India Private Limited: Current Employment. Mohapatra:Cellworks Research India Private Limited: Current Employment. G:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment.

Computational method for approximating the behaviour of a triopoly: an application to the mobile telecommunications sector in Greece

Computational biology models of the Volterra-Lotka family, known as competing species models, are used for modelling a triopoly market, with application to the mobile telecommunications in Greece. Using a data sample for 1999-2016, parameter estimation with nonlinear least squares is performed. The findings show that the proportional change in the market share of the two largest companies, Cosmote and Vodafone, depends negatively on the market share of each other. Further, the market share of the marker leader, Cosmote, depends positively on the market share of the smallest company, Wind. The proportional change in the market share of Wind, depends negatively on the market share of the largest company Cosmote but it depends positively by the change in the market share by the second company, Vodafone. In the long-run it was found that the market shares tend to the stable equilibrium point where all three companies will survive with Cosmote having a projected number after eleven years (in 2030) of approximately 7.3 million subscribers, Vodafone 4.9 and Wind 3.7, the total number of projected market size being approximately 16 million customers.

A Multi-Scale Approach to Modeling E. coli Chemotaxis.

The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium—an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium’s approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.

The computational biological model of plant stomata and its application

<p indent=0mm>Stomata are vital organs for plants to exchange gas and water. They play a key role in balancing the demands for CO₂ in photosynthesis with the need for plants to conserve water. Therefore, a deep understanding of stomatal behavior and effective regulation of stomatal movements have significance for improving plant photosynthesis and water use efficiency. Modeling offers an effective tool to predict the change of stomata. Over the last few decades, many stomatal models, especially stomatal conductance models, were proposed to predict plant–environment interactions in response to the ongoing global climate change. Most of the studies are empirical or models focusing on a limited number of environmental factors that affect stomatal movement. Nonetheless, these models have been successfully applied to predict gas exchange and how plants respond to environmental changes. However, the lack of essential “macro-micro” connections to molecular and cellular mechanics hinders the understanding of stomatal regulation and its broader agricultural, biological, and ecological applications. Guard cells open the stomatal pore by the transport and accumulation of osmotically active solutes, mainly K⁺ and Cl⁻, to drive water uptake and cell expansion. They close the pore by coordinating the release of these solutes through K⁺ and anion channels in the plasma membrane. Over the past half-century, a wealth of knowledge has been generated on guard cell transport, signaling, and homeostasis, resolving the properties of the major transport processes and metabolic pathways for osmotic solute uptake and accumulation, and many of the signaling pathways that control them. However, the complex mechanism of ion exchange hinders further understanding of the stomatal mechanism. Quantitative mathematical modeling is an essential tool for gaining a better understanding of stomata, both to integrate detailed knowledge of individual transporters and to extract the properties emergent from their interactions. The computational model OnGuard offers a unique tool for exploring homeostatic properties emerging from the interactions of ion transport. It has already yielded sufficient detail to guide phenotypic and mutational studies, and it represents a key step towards the “reverse engineering” of stomatal guard cell physiology. Based on rational design and simulation, this method can provide information to improve water use efficiency and carbon assimilation. This software has a user-friendly interface, which can be easily accessed by researchers to manipulate the key elements and parameters in the system for guard cell simulation in plants. In this article, we review the development and research of stomatal modeling, compare the traditional stomatal model and the new computational biology model, and propose the development of stomatal computational biology to promote agricultural development in China.

Simulating a Computational Biological Model, Rather Than Reading, Elicits Changes in Brain Activity during Biological Reasoning.

The creation and analysis of models is integral to all scientific disciplines, and modeling is considered a core competency in undergraduate biology education. There remains a gap in understanding how modeling activities may support changes in students’ neural representations. The aim of this study was to evaluate the effects of simulating a model on undergraduates’ behavioral accuracy and neural response patterns when reasoning about biological systems. During brief tutorials, students (n = 30) either simulated a computer model or read expert analysis of a gene regulatory system. Subsequently, students underwent functional magnetic resonance imaging while responding to system-specific questions and system-general questions about modeling concepts. Although groups showed similar behavioral accuracy, the Simulate group showed higher levels of activation than the Read group in right cuneal and postcentral regions during the system-specific task and in the posterior insula and cingulate gyrus during the system-general task. Students’ behavioral accuracy during the system-specific task correlated with lateral prefrontal brain activity independent of instruction group. Findings highlight the sensitivity of neuroimaging methods for identifying changes in representations that may not be evident at the behavioral level. This work provides a foundation for research on how distinct pedagogical approaches may affect the neural networks students engage when reasoning about biological phenomena.

Introduction to In Silico Modeling to Study ROS Dynamics.

This book chapter is drafted for biologists with experimental experiences in ROS biology but being newcomers in the field of modeling. We start with a general introduction about computational modeling in biology and an overview of software tools suitable for beginners. This chapter encompasses an introduction to computational models with special focus on simulation of ROS dynamics. A step-by-step tutorial follows providing guidance for all relevant model development processes. This course of action gives a comprehensible way to understand the benefits of computational models and to gain the necessary knowledge to build own small equation-based models. Small models can be created without any special programming expertise or in-depth technical and mathematical knowledge. Afterward in the final section, a short overview of pitfalls, challenges, and limitations is provided, combined with suggestions for further reading to improve and expand modeling skills of biologists.

Soft composite based hyperelastic model for anisotropic tissue characterization

Soft tissues are complex anisotropic composite systems comprising of multiple differently oriented layers of fiber embedded within a soft matrix. To date, soft tissues have been mainly characterized using simplified linear elastic material models, isotropic viscoelastic and hyperelastic models, and transversely isotropic models. In such models, the effect of fiber volume fraction (FVF), fiber orientation, and fiber-matrix interactions are missing, inhibiting accurate characterization of anisotropic tissue properties. The current work addresses this literature gap with the development of a novel soft composite based material framework to model tissue anisotropy. In this model, the fiber and matrix are considered as separate hyperelastic materials, and fiber-matrix interaction is modeled using multiplicative decomposition of the deformation gradient. The effect of the individual contribution of the fibers and matrix are introduced into the numerical framework for a single soft composite layer, and fiber orientation effects are incorporated into the strain energy functions. Also, strain energy formulations are developed for multiple soft composite layers with varying fiber orientations and contributions, describing the biomechanical behavior of an entire anisotropic tissue block. Stress-strain relationships were derived from the strain energy equations for a uniaxial mechanical test condition. To validate the model parameters, experimental models of soft composites tested under uniaxial tension were characterized using the novel anisotropic hyperelastic model (R2 = 0.983). To date, such a robust anisotropic hyperelastic composite framework has not been developed, which would be indispensable for experimental characterization of tissues and for improving the fidelity of computational biological models in future.

Abstract A28: Systematic identification of astrocyte-tumor crosstalk regulating brain metastatic tumors

Abstract Twenty-four to 45% of all cancer patients develop brain metastasis (BM) with the majority from lung, breast, and melanoma primary cancers, but few patients with BM live longer than a year. Therefore, it is of paramount importance to obtain a deep understanding of mechanisms that promote BM growth, which could be specifically leveraged to develop novel therapeutic strategies. Astrocytes, the most common type of glial cells comprising ~50% of all human brain cells, are a well-characterized perilesional component of BM. Recent discoveries, including ours, provide compelling evidence that molecular crosstalk between astrocytes and cancer cells is integral to BM development. Although seminal findings indicate that interactions with astrocytes occur at both early and late stages of tumor colonization process, our understanding of the reciprocal astrocyte-cancer cell crosstalk is limited. In our studies, we employed the Cell-Cell Communication Explorer (CCCExplorer), a unique computational biology modeling tool, to systematically identify astrocyte-tumor crosstalk signaling in regulating BM. Syngeneic mouse models for BM from breast and lung cancer were injected with mouse cell lines 4T1 (breast cancer) into Balb/c mice and 393N1 (lung cancer) into C57BL/6 mice through internal carotid artery. Whole-brain astrocytes and tumor cells were purified at day 3 post tumor injection (early astrocyte activation) and were profiled by RNA-seq. Even at early stages of BM, transcriptional responses of astrocytes are dramatic, and different tumor origins can induce similar molecular responses in astrocytes. The RNA-seq data were input into the CCCExplorer software to identify four types of cell-cell interaction signaling, i.e., astrocyte→tumor paracrine signaling, astrocyte↔astrocyte autocrine signaling, tumor→astrocyte paracrine signaling, and tumor↔tumor autocrine signaling. For example, our preliminary results show that 30 receptors, 8 transcription factors, and 9 signaling pathways within the 4T1 breast tumor cells were activated by 27 potential ligands secreted by astrocytes. Pathways known to contribute to brain metastatic tumor growth, including TGF-β, PI3K-AKT, gap junction, and MAPK signaling, were reliably identified. Importantly, our unique approach was also able to discern potentially novel inducers of these pathways, such as PCDH7-EGFR, IHNBB-ACVR2A, IL6-IL6R, and CCL5-CCR5. Using in vitro astrocyte and tumor cell coculture assays and astrocyte-specific gene knockout using lenti-CRIPRv2, we demonstrated the significance of the astrocyte→tumor IL6-IL6R, CCL5-CCR5, and PCDH7-EGFR paracrine signaling in early BM. Given that more complicated paracrine signaling networks may evolve during different stages of BM development, we will continue to use CCCExplorer to identify both anti- and prometastatic stimuli from astrocytes to cancer cells and evaluate potential therapeutic targets through in vitro assays and in vivo animal studies. Citation Format: Haowen Nikola Liu, Jianting Sheng, Hong Zhao, Stephen Wong. Systematic identification of astrocyte-tumor crosstalk regulating brain metastatic tumors [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr A28.

Prediction of Outcomes with a Computational Biology Model in Newly Diagnosed Glioblastoma Patients Treated with Radiation Therapy and Temozolomide

Precision medicine has been most successful in targeting single mutations, but personalized medicine using broader genomic tumor profiles for individual patients is less well developed. We evaluate a genomics-informed computational biology model (CBM) to predict outcomes from standard treatments and to suggest novel therapy recommendations in glioblastoma (GBM). In this retrospective study, 98 patients with newly diagnosed GBM undergoing surgery followed by radiation therapy and temozolomide at a single institution with available genomic data were identified. Incorporating mutational and copy number aberration data, a CBM was used to simulate the response of GBM tumor cells and generate efficacy predictions for radiation therapy (RTeff) and temozolomide (TMZeff). RTeff and TMZeff were evaluated for association with overall survival and progression-free survival in a Cox regression model. To demonstrate a CBM-based individualized therapy strategy, treatment recommendations were generated for each patient by testing a panel of 45 central nervous system-penetrant US Food and Drug Administration-approved agents. High RTeff scores were associated with longer survival on univariable analysis (P < .001), which persisted after controlling for age, extent of resection, performance status, MGMT, and IDH status (P = .017). High RTeff patients had a longer overall survival compared with low RTeff patients (median, 27.7 vs 14.6 months). High TMZeff was also associated with longer survival on univariable analysis (P = .007) but did not hold on multivariable analysis, suggesting an interplay with MGMT status. Among predictions of the 3 most efficacious combination therapies for each patient, only 2.4% (7 of 294) of 2-drug recommendations produced by the CBM included TMZ. CBM-based predictions of RT and TMZ effectiveness were associated with survival in patients with newly diagnosed GBM treated with those therapies, suggesting a possible predictive utility. Furthermore, the model was able to suggest novel individualized monotherapies and combinations. Prospective evaluation of such a personalized treatment strategy in clinical trials is needed.

Identification of Lenalidomide Sensitivity and Resistance Mechanisms in Non-Del(5q) Myelodysplastic Syndromes.

Whereas lenalidomide is an effective therapy for del(5q) MDS patients, a minority of non-del(5q) MDS patients achieve hematologic improvement with lenalidomide. We used computational biology modeling and digital drug simulation to examine genomic data from 56 non-del(5q) MDS patients treated with lenalidomide, and then matched treatment response with molecular pathways. The computer inferred genomic abnormalities associating with lenalidomide treatment response in non-del(5q) MDS to include trisomy 8, del(20q), or RUNX1 loss of function mutations. Genomic abnormalities associating with lenalidomide resistance in non-del(5q) MDS patients included mutations in SF3B1, TET2, WNT3A amplification, MCL1 amplification, and/or PSEN2 amplification. These results may inform protocols for determining appropriateness of lenalidomide in non-del(5q) MDS.

Фантомы в ядерной медицине

The nuclear medicine phantom development is based on the step by step description of the computational and experimental biological object model. Computational phantoms are used for geometry of the object description and simulate physics of particle interactions with matter, while experimental phantoms are used for quality control tests and standardization of functional research protocols. Common examples are the dosimetry planning of radionuclide therapy and post-therapeutic scintigraphy with 131I. This review provides a list of methods for computational and experimental phantoms. Examples of existing phantoms created for the nuclear medicine tasks are also given.

Setting the basis of best practices and standards for curation and annotation of logical models in biology-highlights of the [BC]2 2019 CoLoMoTo/SysMod Workshop.

The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled 'Annotation and curation of computational models in biology', organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements.