Using CellML in Computational Models of Multiscale Physiology

Nickerson, David; Hunter, Peter

doi:10.1109/iembs.2005.1615884

Cited by 8 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A different multiscale framework was developed by Shim et al (2011) and Fernandez et al (2012), aiming at studying the initiation of OA at the bone-cartilage interface due to anterior cruciate ligament damage (ACLD). In the framework (shown in Figure 6), which was coded in CellML (Nickerson and Hunter, 2005), the cell level model involved a bone remodeling algorithm (Pivonka et al, 2008) based on the RANK-RANKL-OPG pathway that predicted the number of active osteoblasts (to deposit bone) and osteoclasts (to absorb bone). Similarly, a cartilage damage prediction model was used (based on the work of Nam et al, 2009), which quantitatively described the action of NF-κB signaling cascade under mechanical stimulation.…”

Section: Examplesmentioning

confidence: 99%

Use of Computational Modeling to Study Joint Degeneration: A Review

Mukherjee

Nazemi

Jonkers

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Osteoarthritis (OA), a degenerative joint disease, is the most common chronic condition of the joints, which cannot be prevented effectively. Computational modeling of joint degradation allows to estimate the patient-specific progression of OA, which can aid clinicians to estimate the most suitable time window for surgical intervention in osteoarthritic patients. This paper gives an overview of the different approaches used to model different aspects of joint degeneration, thereby focusing mostly on the knee joint. The paper starts by discussing how OA affects the different components of the joint and how these are accounted for in the models. Subsequently, it discusses the different modeling approaches that can be used to answer questions related to OA etiology, progression and treatment. These models are ordered based on their underlying assumptions and technologies: musculoskeletal models, Finite Element models, (gene) regulatory models, multiscale models and data-driven models (artificial intelligence/machine learning). Finally, it is concluded that in the future, efforts should be made to integrate the different modeling techniques into a more robust computational framework that should not only be efficient to predict OA progression but also easily allow a patient's individualized risk assessment as screening tool for use in clinical practice.

show abstract

Section: Examplesmentioning

confidence: 99%

Use of Computational Modeling to Study Joint Degeneration: A Review

Mukherjee

Nazemi

Jonkers

et al. 2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…These models are aimed at simulating the dynamic interplay between molecular, cellular, and tissue-level changes in a physiologically relevant manner. Multiscale biological simulation (see Southern et al 2007) has been used for modelling cardiac function (Nickerson & Hunter, 2005), tumor growth (Jiang et al, 2005) skin healing (Smallwood & Holcombe, 2006), immune response (Kirschner et al, 2007), and liver injury (Lerapetritou et al, 2009). The IUPS Physiome Project provides a framework for the multiscale simulation of organ systems as driven by tissue and cellular behavior (Hunter et al, 2008).…”

Section: Multiscale Tissue Modelingmentioning

confidence: 99%

Virtual Tissues in Toxicology

Shah

Wambaugh

2010

Journal of Toxicology and Environmental Health, Part B

View full text Add to dashboard Cite

New approaches are vital for efficiently evaluating human health risk of thousands of chemicals in commerce. In vitro models offer a high-throughput approach for assaying chemical-induced molecular and cellular changes; however, bridging these perturbations to in vivo effects across chemicals, dose, time, and species remains challenging. Technological advances in multiresolution imaging and multiscale simulation are making it feasible to reconstruct tissues in silico. In toxicology, these "virtual" tissues (VT) aim to predict histopathological outcomes from alterations of cellular phenotypes that are controlled by chemical-induced perturbations in molecular pathways. The behaviors of thousands of heterogeneous cells in tissues are simulated discretely using agent-based modeling (ABM), in which computational "agents" mimic cell interactions and cellular responses to the microenvironment. The behavior of agents is constrained by physical laws and biological rules derived from experimental evidence. VT extend compartmental physiologic models to simulate both acute insults as well as the chronic effects of low-dose exposure. Furthermore, agent behavior can encode the logic of signaling and genetic regulatory networks to evaluate the role of different pathways in chemical-induced injury. To extrapolate toxicity across species, chemicals, and doses, VT require four main components: (a) organization of prior knowledge on physiologic events to define the mechanistic rules for agent behavior, (b) knowledge on key chemical-induced molecular effects, including activation of stress sensors and changes in molecular pathways that alter the cellular phenotype, (c) multiresolution quantitative and qualitative analysis of histologic data to characterize and measure chemical-, dose-, and time-dependent physiologic events, and (d) multiscale, spatiotemporal simulation frameworks to effectively calibrate and evaluate VT using experimental data. This investigation presents the motivation, implementation, and application of VT with examples from hepatotoxicity and carcinogenesis.

show abstract

“…This was achieved by using the open-source markup languages FieldML 19,24 and CellML. 15,53 By implementing a generic web-based language, FieldML is able to capture the spatially varying information for both bone and cartilage, whereas CellML is used to define the intracellular networks and paracrine kinetics which determine cell activity. The spatial organization of bone and cartilage was defined with a 2D electron microscope image showing cartilage and subchondral bone (Fig.…”

Section: State-of-the-art Multiscale Modeling Of Load-regulated Bone mentioning

confidence: 99%

Toward Mechanical Systems Biology in Bone

2012

View full text Add to dashboard Cite

Cyclic mechanical loading is perhaps the most important physiological factor regulating bone mass and shape in a way which balances optimal strength with minimal weight. This bone adaptation process spans multiple length and time scales. Forces resulting from physiological exercise at the organ scale are sensed at the cellular scale by osteocytes, which reside inside the bone matrix. Via biochemical pathways, osteocytes orchestrate the local remodeling action of osteoblasts (bone formation) and osteoclasts (bone resorption). Together these local adaptive remodeling activities sum up to strengthen bone globally at the organ scale. To resolve the underlying mechanisms it is required to identify and quantify both cause and effect across the different scales. Progress has been made at the different scales experimentally. Computational models of bone adaptation have been developed to piece together various experimental observations at the different scales into coherent and plausible mechanisms. However additional quantitative experimental validation is still required to build upon the insights which have already been achieved. In this review we discuss emerging as well as state of the art experimental and computational techniques and how they might be used in a mechanical systems biology approach to further our understanding of the mechanisms governing load induced bone adaptation, i.e., ways are outlined in which experimental and computational approaches could be coupled, in a quantitative manner to create more reliable multiscale models of bone.

show abstract

Using CellML in Computational Models of Multiscale Physiology

Cited by 8 publications

References 15 publications

Use of Computational Modeling to Study Joint Degeneration: A Review

Use of Computational Modeling to Study Joint Degeneration: A Review

Virtual Tissues in Toxicology

Toward Mechanical Systems Biology in Bone

Contact Info

Product

Resources

About