Computational Biology Modeling Research Articles

Curating models from BioModels: Developing a workflow for creating OMEX files.

The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium. Once reproduced we manually created a new set of files, with the model information stored by the Systems Biology Markup Language (SBML), and simulation instructions stored by the Simulation Experiment Description Markup Language (SED-ML), and everything included in an Open Modeling EXchange (OMEX) file, which could be used with a variety of simulators to reproduce the same results. On the one hand, the reproducibility procedure of 50 models developed a manual workflow that we would use to build an automatic platform to help users more easily curate and verify models in the future. On the other hand, these exercises allowed us to find the limitations and possible enhancement of the current curation and tooling to verify and curate models.

SpanSeq: similarity-based sequence data splitting method for improved development and assessment of deep learning projects.

The use of deep learning models in computational biology has increased massively in recent years, and it is expected to continue with the current advances in the fields such as Natural Language Processing. These models, although able to draw complex relations between input and target, are also inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to split the available data randomly into development (train/validation) and test sets. This procedure, although standard, has been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database partition method for machine learning that can scale to most biological sequences (genes, proteins and genomes) in order to avoid data leakage between sets. We also explore the effect of not restraining similarity between sets by reproducing the development of two state-of-the-art models on bioinformatics, not only confirming the consequences of randomly splitting databases on the model assessment, but expanding those repercussions to the model development. SpanSeq is available at https://github.com/genomicepidemiology/SpanSeq.

Systems-level computational modeling in ischemic stroke: from cells to patients.

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

Design patterns for the construction of computational biological models.

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.

Morphological Stability for in silico Models of Avascular Tumors

The landscape of computational modeling in cancer systems biology is diverse, offering a spectrum of models and frameworks, each with its own trade-offs and advantages. Ideally, models are meant to be useful in refining hypotheses, to sharpen experimental procedures and, in the longer run, even for applications in personalized medicine. One of the greatest challenges is to balance model realism and detail with experimental data to eventually produce useful data-driven models. We contribute to this quest by developing a transparent, highly parsimonious, first principle in silico model of a growing avascular tumor. We initially formulate the physiological considerations and the specific model within a stochastic cell-based framework. We next formulate a corresponding mean-field model using partial differential equations which is amenable to mathematical analysis. Despite a few notable differences between the two models, we are in this way able to successfully detail the impact of all parameters in the stability of the growth process and on the eventual tumor fate of the stochastic model. This facilitates the deduction of Bayesian priors for a given situation, but also provides important insights into the underlying mechanism of tumor growth and progression. Although the resulting model framework is relatively simple and transparent, it can still reproduce the full range of known emergent behavior. We identify a novel model instability arising from nutrient starvation and we also discuss additional insight concerning possible model additions and the effects of those. Thanks to the framework’s flexibility, such additions can be readily included whenever the relevant data become available.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Targeting mitochondrial bioenergetics by combination treatment with imatinib and dichloroacetate in human erythroleukemic K‑562 and colorectal HCT‑116 cancer cells.

Tumor malignant cells are characterized by dysregulation of mitochondrial bioenergetics due to the 'Warburg effect'. In the present study, this metabolic imbalance was explored as a potential target for novel cancer chemotherapy. Imatinib (IM) downregulates the expression levels of SCΟ2 and FRATAXIN (FXN) genes involved in the heme‑dependent cytochrome c oxidase biosynthesis and assembly pathway in human erythroleukemic IM‑sensitive K‑562 chronic myeloid leukemia cells (K‑562). In the present study, it was investigated whether the treatment of cancer cells with IM (an inhibitor of oxidative phosphorylation) separately, or together with dichloroacetate (DCA) (an inhibitor of glycolysis), can inhibit cell proliferation or cause death. Human K‑562 and IM‑chemoresistant K‑562 chronic myeloid leukemia cells (K‑562R), as well as human colorectal carcinoma cells HCT‑116 (+/+p53) and (‑/‑p53, with double TP53 knock-in disruptions), were employed. Treatments of these cells with either IM (1 or 2µM) and/or DCA (4mΜ) were also assessed for the levels of several process biomarkers including SCO2, FXN, lactate dehydrogenase A, glyceraldehyde‑3‑phosphate dehydrogenase, pyruvate kinase M2, hypoxia inducing factor‑1a, heme oxygenase‑1, NF‑κB, stem cell factor and vascular endothelial growth factor via western blot analysis. Computational network biology models were also applied to reveal the connections between the ten proteins examined. Combination treatment of IM with DCA caused extensive cell death (>75%) in K‑562 and considerable (>45%) in HCT‑116 (+/+p53) cultures, but less in K‑562R and HCT‑116 (‑/‑p53), with the latter deficient in full length p53 protein. Such treatment, markedly reduced reactive oxygen species levels, as measured by flow‑cytometry, in K‑562 cells and affected the oxidative phosphorylation and glycolytic biomarkers in all lines examined. These findings indicated, that targeting of cancer mitochondrial bioenergetics with such a combination treatment was very effective, although chemoresistance to IM in leukemia and the absence of a full length p53 in colorectal cells affected its impact.

Predicting Mutational Effects on Ca2+-Activated Chloride Conduction of TMEM16A Based on a Simulation Study.

The dysfunction and defects of ion channels are associated with many human diseases, especially for loss-of-function mutations in ion channels such as cystic fibrosis transmembrane conductance regulator mutations in cystic fibrosis. Understanding ion channels is of great current importance for both medical and fundamental purposes. Such an understanding should include the ability to predict mutational effects and describe functional and mechanistic effects. In this work, we introduce an approach to predict mutational effects based on kinetic information (including reaction barriers and transition state locations) obtained by studying the working mechanism of target proteins. Specifically, we take the Ca2+-activated chloride channel TMEM16A as an example and utilize the computational biology model to predict the mutational effects of key residues. Encouragingly, we verified our predictions through electrophysiological experiments, demonstrating a 94% prediction accuracy regarding mutational directions. The mutational strength assessed by Pearson's correlation coefficient is -0.80 between our calculations and the experimental results. These findings suggest that the proposed methodology is reliable and can provide valuable guidance for revealing functional mechanisms and identifying key residues of the TMEM16A channel. The proposed approach can be extended to a broad scope of biophysical systems.

K+ and pH homeostasis in plant cells is controlled by a synchronized K+ /H+ antiport at the plasma and vacuolar membrane.

Stomatal movement involves ion transport across the plasma membrane (PM) and vacuolar membrane (VM) of guard cells. However, the coupling mechanisms of ion transporters in both membranes and their interplay with Ca2+ and pH changes are largely unclear. Here, we investigated transporter networks in tobacco guard cells and mesophyll cells using multiparametric live-cell ion imaging and computational simulations. K+ and anion fluxes at both, PM and VM, affected H+ and Ca2+ , as changes in extracellular KCl or KNO3 concentrations were accompanied by cytosolic and vacuolar pH shifts and changes in [Ca2+ ]cyt and the membrane potential. At both membranes, the K+ transporter networks mediated an antiport of K+ and H+ . By contrast, net transport of anions was accompanied by parallel H+ transport, with differences in transport capacity for chloride and nitrate. Guard and mesophyll cells exhibited similarities in K+ /H+ transport but cell type-specific differences in [H+ ]cyt and pH-dependent [Ca2+ ]cyt signals. Computational cell biology models explained mechanistically the properties of transporter networks and the coupling of transport across the PM and VM. Our integrated approach indicates fundamental principles of coupled ion transport at membrane sandwiches to control H+ /K+ homeostasis and points to transceptor-like Ca2+ /H+ -based ion signaling in plant cells.

Diffusion models in bioinformatics and computational biology.

Denoising diffusion models embody a type of generative artificial intelligence that can be applied in computer vision, natural language processing and bioinformatics. In this Review, we introduce the key concepts and theoretical foundations of three diffusion modelling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks and score stochastic differential equations). We then explore their applications in bioinformatics and computational biology, including protein design and generation, drug and small-molecule design, protein-ligand interaction modelling, cryo-electron microscopy image data analysis and single-cell data analysis. Finally, we highlight open-source diffusion model tools and consider the future applications of diffusion models in bioinformatics.

SBOannotator: a Python Tool for the Automated Assignment of Systems Biology Ontology Terms.

The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and reproducible manner is challenging. Using precisely defined ontologies enables the encoding of field-specific knowledge and the association of disparate data types. In computational modeling, the medium for representing domain knowledge is the set of orthogonal structured controlled vocabularies named Systems Biology Ontology (SBO). The SBO terms enable modelers to explicitly define and describe model entities, including their roles and characteristics. Here, we present the first standalone tool that automatically assigns SBO terms to multiple entities of a given SBML model, named the SBOannotator. The main focus lies on the reactions, as the correct assignment of precise SBO annotations requires their extensive classification. Our implementation does not consider only top-level terms but examines the functionality of the underlying enzymes to allocate precise and highly specific ontology terms to biochemical reactions. Transport reactions are examined separately and are classified based on the mechanism of molecule transport. Pseudo-reactions that serve modeling purposes are given reasonable terms to distinguish between biomass production and the import or export of metabolites. Finally, other model entities, such as metabolites and genes, are annotated with appropriate terms. Including SBO annotations in the models will enhance the reproducibility, usability, and analysis of biochemical networks. SBOannotator is freely available from https://github.com/draeger-lab/SBOannotator/. Supplementary data are available at Bioinformatics online.

Computational modeling of DLBCL predicts response to BH3-mimetics

In healthy cells, pro- and anti-apoptotic BCL2 family and BH3-only proteins are expressed in a delicate equilibrium. In contrast, this homeostasis is frequently perturbed in cancer cells due to the overexpression of anti-apoptotic BCL2 family proteins. Variability in the expression and sequestration of these proteins in Diffuse Large B cell Lymphoma (DLBCL) likely contributes to variability in response to BH3-mimetics. Successful deployment of BH3-mimetics in DLBCL requires reliable predictions of which lymphoma cells will respond. Here we show that a computational systems biology approach enables accurate prediction of the sensitivity of DLBCL cells to BH3-mimetics. We found that fractional killing of DLBCL, can be explained by cell-to-cell variability in the molecular abundances of signaling proteins. Importantly, by combining protein interaction data with a knowledge of genetic lesions in DLBCL cells, our in silico models accurately predict in vitro response to BH3-mimetics. Furthermore, through virtual DLBCL cells we predict synergistic combinations of BH3-mimetics, which we then experimentally validated. These results show that computational systems biology models of apoptotic signaling, when constrained by experimental data, can facilitate the rational assignment of efficacious targeted inhibitors in B cell malignancies, paving the way for development of more personalized approaches to treatment.

Reproducibility and FAIR principles: the case of a segment polarity network model.

The issue of reproducibility of computational models and the related FAIR principles (findable, accessible, interoperable, and reusable) are examined in a specific test case. I analyze a computational model of the segment polarity network in Drosophila embryos published in 2000. Despite the high number of citations to this publication, 23years later the model is barely accessible, and consequently not interoperable. Following the text of the original publication allowed successfully encoding the model for the open source software COPASI. Subsequently saving the model in the SBML format allowed it to be reused in other open source software packages. Submission of this SBML encoding of the model to the BioModels database enables its findability and accessibility. This demonstrates how the FAIR principles can be successfully enabled by using open source software, widely adopted standards, and public repositories, facilitating reproducibility and reuse of computational cell biology models that will outlive the specific software used.

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer

BackgroundGeneralizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient’s response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient’s response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.MethodsA single-blinded study was performed using a retrospective database from a single institution (9/2014–12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm3 (≥ 99.9% reduction).ResultsThe cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2− (35%), HR−/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10–7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% – 96.4%; Clopper–Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): − 1.99; 95% CI [− 3.96, − 0.02]; p = 0.043) and clinically assessed pCR (HR: − 1.76; 95% CI [− 3.75, 0.23]; p = 0.054).ConclusionWe demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization.

Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

Identification of adaptor proteins by incorporating deep learning and PSSM profiles.

Adaptor proteins, also known as signal transduction adaptor proteins, are important proteins in signal transduction pathways, and play a role in connecting signal proteins for signal transduction between cells. Studies have shown that adaptor proteins are closely related to some diseases, such as tumors and diabetes. Therefore, it is very meaningful to construct a relevant model to accurately identify adaptor proteins. In recent years, many studies have used a position-specific scoring matrix (PSSM) and neural network methods to identify adaptor proteins. However, ordinary neural network models cannot correlate the contextual information in PSSM profiles well, so these studies usually process 20×N (N>20) PSSM into 20×20 dimensions, which results in the loss of a large amount of protein information; This research proposes an efficient method that combines one-dimensional convolution (1-D CNN) and a bidirectional long short-term memory network (biLSTM) to identify adaptor proteins. The complete PSSM profiles are the input of the model, and the complete information of the protein is retained during the training process. We perform cross-validation during model training and test the performance of the model on an independent test set; in the data set with 1224 adaptor proteins and 11,078 non-adaptor proteins, five indicators including specificity, sensitivity, accuracy, area under the receiver operating characteristic curve (AUC) metric and Matthews correlation coefficient (MCC), were employed to evaluate model performance. On the independent test set, the specificity, sensitivity, accuracy and MCC were 0.817, 0.865, 0.823 and 0.465, respectively. Those results show that our method is better than the state-of-the art methods. This study is committed to improve the accuracy of adaptor protein identification, and laid a foundation for further research on diseases related to adaptor protein. This research provided a new idea for the application of deep learning related models in bioinformatics and computational biology.

Computational systems biology approach for permanent tumor elimination and normal tissue protection using negative biasing: Experimental validation in malignant melanoma as case study.

Complete spontaneous tumor regression (without treatment) is well documented to occur in animals and humans as epidemiological analysis show, whereby the malignancy is permanently eliminated. We have developed a novel computational systems biology model for this unique phenomenon to furnish insight into the possibility of therapeutically replicating such regression processes on tumors clinically, without toxic side effects. We have formulated oncological informatics approach using cell-kinetics coupled differential equations while protecting normal tissue. We investigated three main tumor-lysis components: (ⅰ) DNA blockade factors, (ⅱ) Interleukin-2 (IL-2), and (ⅲ) Cytotoxic T-cells (CD8+ T). We studied the temporal variations of these factors, utilizing preclinical experimental investigations on malignant tumors, using mammalian melanoma microarray and histiocytoma immunochemical assessment. We found that permanent tumor regression can occur by: 1) Negative-Bias shift in population trajectory of tumor cells, eradicating them under first-order asymptotic kinetics, and 2) Temporal alteration in the three antitumor components (DNA replication-blockade, Antitumor T-lymphocyte, IL-2), which are respectively characterized by the following patterns: (a) Unimodal Inverted-U function, (b) Bimodal M-function, (c) Stationary-step function. These provide a time-wise orchestrated tri-phasic cytotoxic profile. We have also elucidated gene-expression levels corresponding to the above three components: (ⅰ) DNA-damage G2/M checkpoint regulation [genes: CDC2-CHEK], (ⅱ) Chemokine signaling: IL-2/15 [genes: IL2RG-IKT3], (ⅲ) T-lymphocyte signaling (genes: TRGV5-CD28). All three components quantitatively followed the same activation profiles predicted by our computational model (Smirnov-Kolmogorov statistical test satisfied, α = 5%). We have shown that the genes CASP7-GZMB are signatures of Negative-bias dynamics, enabling eradication of the residual tumor. Using the negative-biasing principle, we have furnished the dose-time profile of equivalent therapeutic agents (DNA-alkylator, IL-2, T-cell input) so that melanoma tumor may therapeutically undergo permanent extinction by replicating the spontaneous tumor regression dynamics.

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

Relapsed glioblastoma (GBM) is often an imminently fatal condition with limited therapeutic options. Computation biological modeling, i.e., biosimulation, of comprehensive genomic information affords the opportunity to create a disease avatar that can be interrogated in silico with various drug combinations to identify the most effective therapies. We report the outcome of a GBM patient with chromosome 12q amplification who achieved substantial disease remission from a novel therapy using this approach. Following next generation sequencing (NGS) was performed on the tumor specimen. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotype. In silico responses to various drug combinations were biosimulated in the disease network. Efficacy scores representing the computational effect of treatment for each strategy were generated and compared to each other to ascertain the differential benefit in drug response from various regimens. Biosimulation identified CDK4/6 inhibitors, nelfinavir and leflunomide to be effective agents singly and in combination. Upon receiving this treatment, the patient achieved a prompt and clinically meaningful remission lasting 6 months. Biosimulation has utility to identify active treatment combinations, stratify treatment options and identify investigational agents relevant to patients' comprehensive genomic abnormalities. Additionally, the combination of abemaciclib and nelfinavir appear promising for GBM and potentially other cancers harboring chromosome 12q amplification.

Biomechanical response of soft tissues during passage of synovial fluid in compression

The present study provides a novelty approach for the computational biological model based on continuum mixture theory in combination with power-law model for incorporating an accurate governing model for the synovial fluids. We investigated the biomechanical response of a soft tissue while passage of non-Newtonian fluid during act of loading at the rigid bony interface. A special kind of multiphasic deformation has been reported in these types of problems that justify nonlinear coupling between the fluid and solid. In modeling these types of problems, general assumption of mixture constituents incompressibility is often provoked. The mixture components are considered intrinsically incompressible; however, in the derivation of governing equations, viscoelastic behavior of the solid along with interstitial fluid was developed. The nonlinear interaction between the fluid–solid is modeled using strain-dependent permeability and is experimentally determined. This manipulation of linear model with nonlinear permeability required attention for the computational point of view. A system of nonlinear coupled partial differential equations is derived for the local fluid pressure along with an equation for solid deformation. The governing system of equations is solved numerically for the case of permeability dependent flow, whereas an exact solution is given for constant permeability case. Various interesting features, such as, pressure changes within the tissue, swelling behavior of the solid matrix, and effects of power law index on the tissue deformation have been presented graphically. A good qualitative agreement has been noticed between the exact and numerical solutions for constant permeability case.

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

Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.