CytoSolve: A Scalable Computational Method for Dynamic Integration of Multiple Molecular Pathway Models

Attenuation of Aging-Related Oxidative Stress Pathways by Phytonutrients: A Computational Systems Biology Analysis

Aging results from gradual accumulation of damage to the cellular functions caused by biochemical processes such as oxidative stress, inflammation-driven prolonged cellular senescence state, immune system malfunction, psychological stress, and epigenetic changes due to exposure to environmental toxins. Plant-derived bioactive molecules have been shown to ameliorate the damage from oxidative stress. This research seeks to uncover the mechanisms of action of how phytochemicals from fruit/berry/vegetable (FBV) juice powder mitigate oxidative stress. The study uses a computational systems biology approach to (1) identify biomolecular pathways of oxidative stress; (2) identify phytochemicals from FBV juice powder and their specific action on oxidative stress mechanisms; and (3) quantitatively estimate the effects of FBV juice powder bioactive compounds on oxidative stress. The compounds in FBV affected two oxidative stress molecular pathways: (1) reactive oxygen species (ROS) production and (2) antioxidant enzyme production. Six bioactive compounds including cyanidin, delphinidin, ellagic acid, kaempherol, malvidin, and rutin in FBV significantly lowered production of ROS and increased the production of antioxidant enzymes such as catalase, heme oxygenase-1, superoxide dismutase, and glutathione peroxidase. FBV juice powder provides a combination of bioactive compounds that attenuate aging by affecting multiple pathways of oxidative stress.

Mechanistic Understanding of D-Glucaric Acid to Support Liver Detoxification Essential to Muscle Health Using a Computational Systems Biology Approach

Liver and muscle health are intimately connected. Nutritional strategies that support liver detoxification are beneficial to muscle recovery. Computational-in silico-molecular systems' biology analysis of supplementation of calcium and potassium glucarate salts and their metabolite D-glucaric acid (GA) reveals their positive effect on mitigation of liver detoxification via four specific molecular pathways: (1) ROS production, (2) deconjugation, (3) apoptosis of hepatocytes, and (4) β-glucuronidase synthesis. GA improves liver detoxification by downregulating hepatocyte apoptosis, reducing glucuronide deconjugates levels, reducing ROS production, and inhibiting β-Glucuronidase enzyme that reduces re-absorption of toxins in hepatocytes. Results from this in silico study provide an integrative molecular mechanistic systems explanation for the mitigation of liver toxicity by GA.

Molecular Systems Architecture of Interactome in the Acute Myeloid Leukemia Microenvironment

A molecular systems architecture is presented for acute myeloid leukemia (AML) to provide a framework for organizing the complexity of biomolecular interactions. AML is a multifactorial disease resulting from impaired differentiation and increased proliferation of hematopoietic precursor cells involving genetic mutations, signaling pathways related to the cancer cell genetics, and molecular interactions between the cancer cell and the tumor microenvironment, including endothelial cells, fibroblasts, myeloid-derived suppressor cells, bone marrow stromal cells, and immune cells (e.g., T-regs, T-helper 1 cells, T-helper 17 cells, T-effector cells, natural killer cells, and dendritic cells). This molecular systems architecture provides a layered understanding of intra- and inter-cellular interactions in the AML cancer cell and the cells in the stromal microenvironment. The molecular systems architecture may be utilized for target identification and the discovery of single and combination therapeutics and strategies to treat AML.

Bioactive compounds in green tea may improve transplant tolerance: A computational systems biology analysis

BACKGROUND

Green tea (Camellia sinensis) has bioactive compounds that have been shown to possess nutritive effects on various biomolecular processes such as immunomodulation. This research explores the immunomodulatory effects of green tea in reducing transplant rejection.

METHOD

The study employs computational systems biology: 1) to identify biomolecular mechanisms of immunomodulation in transplant rejection; 2) to identify the bioactive compounds of green tea and their specific effects on mechanisms of immunomodulation in transplant rejection; and, 3) to predict the quantitative effects of those bioactive compounds on immunomodulation in transplant rejection.

RESULTS

Three bioactive compounds of green tea - epicatechin (EC), gallic acid (GA), and epigallocatechin gallate (EGCG), were identified for their potential effects on immunomodulation of transplant rejection. Of the three, EGCG was the only one determined to enhance anti-inflammatory activity by: 1) upregulating synthesis of HO-1 that is known to promote Treg and Th2 phenotypes associated with enabling transplant tolerance; and, 2) downregulating pro-inflammatory cytokines IL-2, IL-17, IFN-γ, TNF-α, NO, IL-6, and IL-1β that are known to promote Th1 and Th17 phenotypes associated with transplant rejection.

CONCLUSIONS

To the best of our knowledge, this study provides the first molecular mechanistic understanding the clinical nutritive value of green tea, specifically the bioactive compound EGCG, in enabling transplant tolerance.

Multi-Criteria Decision Analysis Model for Assessing the Risk from Multi-Ingredient Dietary Supplements (MIDS)

Military personnel use dietary supplements (DS) for performance enhancement, bodybuilding, weight loss, and to maintain health. Adverse events, including cardiovascular (CV) effects, have been reported in military personnel taking supplements. Previous research determined that ingestion of multi-ingredient dietary supplements (MIDS), can lead to signals of safety concerns. Therefore, to assess the safety of MIDS, the Department of Defense via a contractor explored the development of a model-based risk assessment tool. We present a strategy and preliminary novel multi-criteria decision analysis (MCDA)-based tool for assessing the risk of adverse CV effects from MIDS. The tool integrates toxicology and other relevant data available on MIDS; likelihood of exposure, and biologic plausibility that could contribute to specific aspects of risk.Inputs for the model are values of four measures assigned based on the available evidence supplemented with the opinion of experts in toxicology, modeling, risk assessment etc. Measures were weighted based on the experts' assessment of measures' relative importance. Finally, all data for the four measures were integrated to provide a risk potential of 0 (low risk) to 100 (high risk) that defines the relative risk of a MIDS to cause adverse reactions.We conclude that the best available evidence must be supplemented with the opinion of experts in medicine, toxicology and pharmacology. Model-based approaches are useful to inform risk assessment in the absence of data. This MCDA model provides a foundation for refinement and validation of accuracy of the model predictions as new evidence becomes available.

A case for the reuse and adaptation of mechanistic computational models to study transplant immunology

Computational mechanistic models constitute powerful tools for summarizing our knowledge in quantitative terms, providing mechanistic understanding, and generating new hypotheses. The present review emphasizes the advantages of reusing publicly available computational models as a way to capitalize on existing knowledge, reduce the number of parameters that need to be adjusted to experimental data, and facilitate hypothesis generation. Finally, it includes a step-by-step example of the reuse and adaptation of an existing model of immune responses to tuberculosis, tumor growth, and blood pathogens, to study donor-specific antibody (DSA) responses. This review aims to illustrate the benefit of leveraging the currently available computational models in immunology to accelerate the study of alloimmune responses, and to encourage modelers to share their models to further advance our understanding of transplant immunology.

Pericytes of the neurovascular unit: key functions and signaling pathways

Pericytes are vascular mural cells embedded in the basement membrane of blood microvessels. They extend their processes along capillaries, pre-capillary arterioles and post-capillary venules. CNS pericytes are uniquely positioned in the neurovascular unit between endothelial cells, astrocytes and neurons. They integrate, coordinate and process signals from their neighboring cells to generate diverse functional responses that are critical for CNS functions in health and disease, including regulation of the blood-brain barrier permeability, angiogenesis, clearance of toxic metabolites, capillary hemodynamic responses, neuroinflammation and stem cell activity. Here we examine the key signaling pathways between pericytes and their neighboring endothelial cells, astrocytes and neurons that control neurovascular functions. We also review the role of pericytes in CNS disorders including rare monogenic diseases and complex neurological disorders such as Alzheimer's disease and brain tumors. Finally, we discuss directions for future studies.

Modeling oxygen requirements in ischemic cardiomyocytes

Heart disease remains the leading cause of death globally. Although reperfusion following myocardial ischemia can prevent death by restoring nutrient flow, ischemia/reperfusion injury can cause significant heart damage. The mechanisms that drive ischemia/reperfusion injury are not well understood; currently, few methods can predict the state of the cardiac muscle cell and its metabolic conditions during ischemia. Here, we explored the energetic sustainability of cardiomyocytes, using a model for cellular metabolism to predict the levels of ATP following hypoxia. We modeled glycolytic metabolism with a system of coupled ordinary differential equations describing the individual metabolic reactions within the cardiomyocyte over time. Reduced oxygen levels and ATP consumption rates were simulated to characterize metabolite responses to ischemia. By tracking biochemical species within the cell, our model enables prediction of the cell's condition up to the moment of reperfusion. The simulations revealed a distinct transition between energetically sustainable and unsustainable ATP concentrations for various energetic demands. Our model illustrates how even low oxygen concentrations allow the cell to perform essential functions. We found that the oxygen level required for a sustainable level of ATP increases roughly linearly with the ATP consumption rate. An extracellular O₂ concentration of ∼0.007 mm could supply basic energy needs in non-beating cardiomyocytes, suggesting that increased collateral circulation may provide an important source of oxygen to sustain the cardiomyocyte during extended ischemia. Our model provides a time-dependent framework for studying various intervention strategies to change the outcome of reperfusion.

Challenges in horizontal model integration

Background

Systems Biology has motivated dynamic models of important intracellular processes at the pathway level, for example, in signal transduction and cell cycle control. To answer important biomedical questions, however, one has to go beyond the study of isolated pathways towards the joint study of interacting signaling pathways or the joint study of signal transduction and cell cycle control. Thereby the reuse of established models is preferable, as it will generally reduce the modeling effort and increase the acceptance of the combined model in the field.

Results

Obtaining a combined model can be challenging, especially if the submodels are large and/or come from different working groups (as is generally the case, when models stored in established repositories are used). To support this task, we describe a semi-automatic workflow based on established software tools. In particular, two frequent challenges are described: identification of the overlap and subsequent (re)parameterization of the integrated model.

Conclusions

The reparameterization step is crucial, if the goal is to obtain a model that can reproduce the data explained by the individual models. For demonstration purposes we apply our workflow to integrate two signaling pathways (EGF and NGF) from the BioModels Database.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0266-3) contains supplementary material, which is available to authorized users.

Integrating Multiscale Modeling with Drug Effects for Cancer Treatment

In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system's pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multi factorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-exposure responses and economic potentials of drugs. Thus, it requires multiscale methods to address the need for integrating models from the molecular levels to the cellular, tissue, and organism levels. It is a common belief that tumorigenesis and tumor growth can be best understood and tackled by employing and integrating a multifaceted approach that includes in vivo and in vitro experiments, in silico models, multiscale tumor modeling, continuous/discrete modeling, agent-based modeling, and multiscale modeling with PK/PD drug effect inputs. We provide an example application of multiscale modeling employing stochastic hybrid system for a colon cancer cell line HCT-116 with the application of Lapatinib drug. It is observed that the simulation results are similar to those observed from the setup of the wet-lab experiments at the Translational Genomics Research Institute.

The great escape; the hallmarks of resistance to antiangiogenic therapy

The concept of antiangiogenic therapy in cancer treatment has led to the approval of different agents, most of them targeting the well known vascular endothelial growth factor pathway. Despite promising results in preclinical studies, the efficacy of antiangiogenic therapy in the clinical setting remains limited. Recently, awareness has emerged on resistance to antiangiogenic therapies. It has become apparent that the intricate complex interplay between tumors and stromal cells, including endothelial cells and associated mural cells, allows for escape mechanisms to arise that counteract the effects of these targeted therapeutics. Here, we review and discuss known and novel mechanisms that contribute to resistance against antiangiogenic therapy and provide an outlook to possible improvements in therapeutic approaches.

Simulation environment and graphical visualization environment: a COPD use-case

BACKGROUND

Today, many different tools are developed to execute and visualize physiological models that represent the human physiology. Most of these tools run models written in very specific programming languages which in turn simplify the communication among models. Nevertheless, not all of these tools are able to run models written in different programming languages. In addition, interoperability between such models remains an unresolved issue.

RESULTS

In this paper we present a simulation environment that allows, first, the execution of models developed in different programming languages and second the communication of parameters to interconnect these models. This simulation environment, developed within the Synergy-COPD project, aims at helping and supporting bio-researchers and medical students understand the internal mechanisms of the human body through the use of physiological models. This tool is composed of a graphical visualization environment, which is a web interface through which the user can interact with the models, and a simulation workflow management system composed of a control module and a data warehouse manager. The control module monitors the correct functioning of the whole system. The data warehouse manager is responsible for managing the stored information and supporting its flow among the different modules.

CONCLUSION

It has been proved that the simulation environment presented here allows the user to research and study the internal mechanisms of the human physiology by the use of models via a graphical visualization environment. A new tool for bio-researchers is ready for deployment in various use cases scenarios.

In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and 3), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.

The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models

Background

The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.

Results

A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells.

Conclusions

The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.

An online model composition tool for system biology models

Background

There are multiple representation formats for Systems Biology computational models, and the Systems Biology Markup Language (SBML) is one of the most widely used. SBML is used to capture, store, and distribute computational models by Systems Biology data sources (e.g., the BioModels Database) and researchers. Therefore, there is a need for all-in-one web-based solutions that support advance SBML functionalities such as uploading, editing, composing, visualizing, simulating, querying, and browsing computational models.

Results

We present the design and implementation of the Model Composition Tool (Interface) within the PathCase-SB (PathCase Systems Biology) web portal. The tool helps users compose systems biology models to facilitate the complex process of merging systems biology models. We also present three tools that support the model composition tool, namely, (1) Model Simulation Interface that generates a visual plot of the simulation according to user’s input, (2) iModel Tool as a platform for users to upload their own models to compose, and (3) SimCom Tool that provides a side by side comparison of models being composed in the same pathway. Finally, we provide a web site that hosts BioModels Database models and a separate web site that hosts SBML Test Suite models.

Conclusions

Model composition tool (and the other three tools) can be used with little or no knowledge of the SBML document structure. For this reason, students or anyone who wants to learn about systems biology will benefit from the described functionalities. SBML Test Suite models will be a nice starting point for beginners. And, for more advanced purposes, users will able to access and employ models of the BioModels Database as well.

Contribution of genome-wide association studies to scientific research: a pragmatic approach to evaluate their impact

The factual value of genome-wide association studies (GWAS) for the understanding of multifactorial diseases is a matter of intense debate. Practical consequences for the development of more effective therapies do not seem to be around the corner. Here we propose a pragmatic and objective evaluation of how much new biology is arising from these studies, with particular attention to the information that can help prioritize therapeutic targets. We chose multiple sclerosis (MS) as a paradigm disease and assumed that, in pre-GWAS candidate-gene studies, the knowledge behind the choice of each gene reflected the understanding of the disease prior to the advent of GWAS. Importantly, this knowledge was based mainly on non-genetic, phenotypic grounds. We performed single-gene and pathway-oriented comparisons of old and new knowledge in MS by confronting an unbiased list of candidate genes in pre-GWAS association studies with those genes exceeding the genome-wide significance threshold in GWAS published from 2007 on. At the single gene level, the majority (94 out of 125) of GWAS-discovered variants had never been contemplated as plausible candidates in pre-GWAS association studies. The 31 genes that were present in both pre- and post-GWAS lists may be of particular interest in that they represent disease-associated variants whose pathogenetic relevance is supported at the phenotypic level (i.e. the phenotypic information that steered their selection as candidate genes in pre-GWAS association studies). As such they represent attractive therapeutic targets. Interestingly, our analysis shows that some of these variants are targets of pharmacologically active compounds, including drugs that are already registered for human use. Compared with the above single-gene analysis, at the pathway level GWAS results appear more coherent with previous knowledge, reinforcing some of the current views on MS pathogenesis and related therapeutic research. This study presents a pragmatic approach that helps interpret and exploit GWAS knowledge.

Setting a research agenda for progressive multiple sclerosis: the International Collaborative on Progressive MS

Despite significant progress in the development of therapies for relapsing MS, progressive MS remains comparatively disappointing. Our objective, in this paper, is to review the current challenges in developing therapies for progressive MS and identify key priority areas for research. A collaborative was convened by volunteer and staff leaders from several MS societies with the mission to expedite the development of effective disease-modifying and symptom management therapies for progressive forms of multiple sclerosis. Through a series of scientific and strategic planning meetings, the collaborative identified and developed new perspectives on five key priority areas for research: experimental models, identification and validation of targets and repurposing opportunities, proof-of-concept clinical trial strategies, clinical outcome measures, and symptom management and rehabilitation. Our conclusions, tackling the impediments in developing therapies for progressive MS will require an integrated, multi-disciplinary approach to enable effective translation of research into therapies for progressive MS. Engagement of the MS research community through an international effort is needed to address and fund these research priorities with the ultimate goal of expediting the development of disease-modifying and symptom-relief treatments for progressive MS.

Combinatorial drug therapy for cancer in the post-genomic era

Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

OREMPdb: a semantic dictionary of computational pathway models

Background

The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.

Methods

Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.

Results

OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared.

Multiscale mathematical modeling to support drug development

It is widely recognized that major improvements are required in the methods currently being used to develop new therapeutic drugs. The time from initial target identification to commercialization can be 10-14 years and incur a cost in the hundreds of millions of dollars. Even after substantial investment, only 30-40% of the candidate compounds entering clinical trials are successful. We propose that multiscale mathematical pathway modeling can be used to decrease time required to bring candidate drugs to clinical trial and increase the probability that they will be successful in humans. The requirements for multiple time scales and spatial scales are discussed, and new computational paradigms are identified to address the increased complexity of modeling.

Module-based multiscale simulation of angiogenesis in skeletal muscle

Background

Mathematical modeling of angiogenesis has been gaining momentum as a means to shed new light on the biological complexity underlying blood vessel growth. A variety of computational models have been developed, each focusing on different aspects of the angiogenesis process and occurring at different biological scales, ranging from the molecular to the tissue levels. Integration of models at different scales is a challenging and currently unsolved problem.

Results

We present an object-oriented module-based computational integration strategy to build a multiscale model of angiogenesis that links currently available models. As an example case, we use this approach to integrate modules representing microvascular blood flow, oxygen transport, vascular endothelial growth factor transport and endothelial cell behavior (sensing, migration and proliferation). Modeling methodologies in these modules include algebraic equations, partial differential equations and agent-based models with complex logical rules. We apply this integrated model to simulate exercise-induced angiogenesis in skeletal muscle. The simulation results compare capillary growth patterns between different exercise conditions for a single bout of exercise. Results demonstrate how the computational infrastructure can effectively integrate multiple modules by coordinating their connectivity and data exchange. Model parameterization offers simulation flexibility and a platform for performing sensitivity analysis.

Conclusions

This systems biology strategy can be applied to larger scale integration of computational models of angiogenesis in skeletal muscle, or other complex processes in other tissues under physiological and pathological conditions.