Harmonizing semantic annotations for computational models in biology

Computational Modeling of Protein Networks Predicts Treatment Outcomes in Multiple Myeloma (MM)

A cornerstone of systems biology is the use of computational modeling, by which hypotheses can be cast into a quantitative form that can be tested systematically. The use of computational modeling by biologists promises to pave the way for more rigorous analyses of biological functions, and ultimately will lead to new and better treatments for disease.A crucial enabler for more widespread use of computational modeling in biology is reaching agreement on how to represent, store, and communicate models between software tools. The Systems Biology Markup Language (SBML) project is an effort to create a machine-readable format for representing computational models in biology. By supporting SBML as an input and output format, different software tools can operate on the same representation of a model, removing chances for errors in translation and assuring a common starting point for analyses and simulations. SBML has become the most successful effort in this direction so far, with over 100 software systems supporting it today.In this presentation, I will discuss the current state of SBML, including recent developments such as this year's finalization of Version 2 of SBML Level 2. I will also survey some of the software tools that support SBML, and related projects that have arisen to support more effective use of computational models. Lastly, I will discuss expected future developments in SBML.

Maximising FAIRness of biosimulation models requires a comprehensive description of model entities such as reactions, variables, and components. The COmputational Modeling in BIology NEtwork (COMBINE) community encourages the use of Resource Description Framework with composite annotations that semantically involve ontologies to ensure completeness and accuracy. These annotations facilitate scientists to find models or detailed information to inform further reuse, such as model composition, reproduction, and curation. SPARQL has been recommended as a key standard to access semantic annotation with RDF, which helps get entities precisely. However, SPARQL is unsuitable for most repository users who explore biosimulation models freely without adequate knowledge of ontologies, RDF structure, and SPARQL syntax. We propose here a text-based information retrieval approach, CASBERT, that is easy to use and can present candidates of relevant entities from models across a repository’s contents. CASBERT adapts Bidirectional Encoder Representations from Transformers (BERT), where each composite annotation about an entity is converted into an entity embedding for subsequent storage in a list of entity embeddings. For entity lookup, a query is transformed to a query embedding and compared to the entity embeddings, and then the entities are displayed in order based on their similarity. The list structure makes it possible to implement CASBERT as an efficient search engine product, with inexpensive addition, modification, and insertion of entity embedding. To demonstrate and test CASBERT, we created a dataset for testing from the Physiome Model Repository and a static export of the BioModels database consisting of query-entities pairs. Measured using Mean Average Precision and Mean Reciprocal Rank, we found that our approach can perform better than the traditional bag-of-words method.

A Study to Predict Response to Azacitidine or Lenalidomide/Azacitidine Combination Therapy in MDS and AML Patients Using a Computational Biological Modeling (CBM) Approach

Hypomethylating agents (HMA) and lenalidomide (LEN) are approved and used in the treatment of patients (pts) with MDS, though these drugs fail in most pts. No method exists to predict drug response beyond associating single actionable mutations with a single drug's response. We hypothesized that MDS pts can be clustered by similarities in genomic/molecular profiles, & that each cluster may be assigned combos of FDA-approved drugs to target their unique biomarker profile. Bone marrow cells from 88 MDS pts & the MDS-L cell line were analyzed by cytogenetics & for mutations in 14 myeloid genes using NGS. 31 pts had sufficient data for analysis. 20 profiles had similar aberrations & were grouped. Genomic data from pts and MDS-L were entered into a computational biology modeling (CBM) software, which generates a disease-specific protein network map using PubMed to create digital models and identify characteristic biomarkers unique to each pt. An algorithm was created to cluster the models based on overlapping disease-specific biomarkers. Digital drug simulations (DDS) were conducted both on MDS-L & pt simulation models by quantitatively measuring drug effect on a cell growth score (CGS), a composite of cell proliferation, viability & apoptosis. DDS identified drugs by assessing their impact on disease-specific biomarkers and calculated CGSs. Predictions were validated using MTT. 14/31 MDS pt profiles, including the MDS-L cell line, clustered into 4 groups based on biomarker characteristics.MDS-L cells harbor NRAS (G12A) mutation,-7,-12,+1,+8,+19,+20 and +21. Genes associated with increased copy number (CN) include AURKA, IGFR, PAR5, MTOR, IL6, JAK3, MDM4, MYC, MCL1, COX2, PDE4A, and RCE1; genes associated with decreased CN include DUSP1, RASA1, NR3C1, IRF1, ETV6, and SHH. CBM identified active biomarkers in MDS-L cells (90RSK, MAPK7, AKT and BTK), validated by western blot. DDS predicted nelfinavir+celecoxib to be effective in MDS-L. MDS-L cells were treated with nelfinavir, celecoxib, and nelfinavir+celecoxib with increasing doses. Nelfinavir & celecoxib reduced MDS-L viability in a dose-dependent manner, while nelfinavir+celecoxib showed additive reduction of MDS-L viability. DDS was performed on each pt to predict response to HMA and LEN. Two of the clusters (n=2/cluster) were predicted to be non-responders to any SOC therapy. A third cluster (n=3) was predicted to respond to HMA, but not LEN, and the fourth cluster (n=6) showed varying or no response to either HMA/LEN. These results use a novel concept of using genomics & CBM to cluster profiles with overlapping disease-relevant biomarkers & similar drug response predictions. CBM can identify pt populations who may benefit from certain therapeutic regimens, improving response rates, & give insights into the mechanisms by which each drug impacts the MDS-specific biomarkers Citation Format: Leylah M. Drusbosky, Kimberly E. Hawkins, Amy Meacham, Elizabeth Wise, Neeraj Kumar Singh, Chandan Kumar, Sumanth M. Vasista, Rakhi P. Suseela, Taher Abbasi, Shireen Vali, Kaoru Tohyama, Maher Albitar, Peter P. Sayeski, Christopher R. Cogle. Genomics-driven clustering of disease-related biomarkers identifies therapeutic options in myelodysplastic syndromes (MDS) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4285.

Correction to: Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.

Predicting Response to CDK4/6 Inhibitors and Combinations Using a Computational Biology Model and Its Validation: A Beat AML Project Study

Mechanisms of Azacitidine Chemotherapy Resistance in AML and MDS and New Therapy Options

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

ObjectiveThe Systems Biology Markup Language (SBML) is a popular open format for storing and exchanging computational models in biology. The definition of SBML is captured in formal specification documents. SBMLPkgSpec is a LaTeX document style intended to fill the need for a standard format for writing such specification documents.ResultsSpecification documents for SBML Level 3 extensions (known as packages in SBML) are made more uniform with the use of a standard template. SBMLPkgSpec is a LaTeX class that provides a document framework for SBML Level 3 package specifications, to simplify the work of document authors while improving the overall quality of the family of SBML specifications.

<p>These are the conference proceedings of COMBINE 2019, the 10th Computational Modeling in Biology Network (COMBINE) meeting that took place in Heidelberg (Germany) from July 15th to July 19th, 2019.</p> <p>The Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of the various community standards and formats for computational models in the life sciences. It was created in 2010 to enable the sharing of resources, tools, and other infrastructure, and to coordinate standardization efforts for modeling in biology. COMBINE brings standard communities together around activities that are mutually beneficial. These activities include making specification documents available from a common location, providing a central point of contact, and organizing regular face-to-face meetings. To this end, the COMBINE network organizes an annual conference style meeting (the COMBINE Forum) and annual hackathon style events called HARMONY (Hackathon on Resources for Modeling in Biology), as well as tutorials and training events.</p> <p>At COMBINE 2019 a combination of keynote lectures, invited talks and interactive breakout discussions, as well as contributed talks, posters and lightning talks, selected from submitted abstracts, provided the basis for the meeting, offering diverse formats to exchange information, to discuss and work on interoperability problems and to demonstrate support for standards implemented in modelling tools, platforms and databases. One special focus of COMBINE 2019 was on the standardization need in systems medicine, which has been recognized as a necessity for computer-assisted personalized medicine. To direct attention to this topic, the European standardization framework for data integration and data-driven in silico models (EUSTANDS4PM) organized a workshop as part of COMBINE 2019. Also, reproducibility in modelling was a main topic, as reflected by several sessions and workshops. Besides these focus themes, also sessions, workshops and breakout discussions around single standards, their further development and their interoperability helped to advance the standardization, standing in the tradition of COMBINE.</p>

With the completion of genomes of many species and the advances of microarray technologies, we begin to possess a tremendous amount of valuable biological data — but these raw products are still far from usable. One of the most challenging problems of this century is to decipher this huge amount of biological information, turning the data into knowledge. The past decade has witnessed a number of successful applications of statistical models in computational biology. This article focuses on one of these success stories: Using Bayesian models and Monte Carlo methods to find short repetitive patterns in a set of DNA or protein sequences, a task often referred to as themotif discovery. We review a few probabilistic models that have recently been shown useful for motif discovery and provide a novel framework based on a Bayesian segmentation model to unify these approaches. We show how to combine the dictionary model with the Gibbs sampler and how a segmentation-based data augmentation scheme can be implemented. A few interesting open problems are also discussed.

With the release of the landmark report Toxicity Testing in the 21st Century: A Vision and a Strategy, the U.S. National Academy of Sciences, in 2007, precipitated a major change in the way toxicity testing is conducted. It envisions increased efficiency in toxicity testing and decreased animal usage by transitioning from current expensive and lengthy in vivo testing with qualitative endpoints to in vitro toxicity pathway assays on human cells or cell lines using robotic high-throughput screening with mechanistic quantitative parameters. Risk assessment in the exposed human population would focus on avoiding significant perturbations in these toxicity pathways. Computational systems biology models would be implemented to determine the dose-response models of perturbations of pathway function. Extrapolation of in vitro results to in vivo human blood and tissue concentrations would be based on pharmacokinetic models for the given exposure condition. This practice would enhance human relevance of test results, and would cover several test agents, compared to traditional toxicological testing strategies. As all the tools that are necessary to implement the vision are currently available or in an advanced stage of development, the key prerequisites to achieving this paradigm shift are a commitment to change in the scientific community, which could be facilitated by a broad discussion of the vision, and obtaining necessary resources to enhance current knowledge of pathway perturbations and pathway assays in humans and to implement computational systems biology models. Implementation of these strategies would result in a new toxicity testing paradigm firmly based on human biology.

Model repositories such as the BioModels Database or the CellML Model Repository are frequently accessed to retrieve computational models describing biological systems. However, the current designs of these databases limit the types of supported queries, and many data in these repositories cannot easily be accessed. Computational methods for model retrieval cannot be applied. In this paper we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models' structure, incorporates semantic annotations and experiment descriptions, and ultimately connects different types of model-related data.The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features such as network structure.The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching, filtering etc.We exemplify how CellML- and SBML-encoded models can be maintained in one database, how these models can be linked via annotations, and queried.

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.