Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

Insights into the Role of Chemokines, Damage-Associated Molecular Patterns, and Lymphocyte-Derived Mediators from Computational Models of Trauma-Induced Inflammation

Significance: Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models. Recent Advances: Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies. Critical Issues: Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest (i) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a damage-associated molecular pattern/Toll-like receptor-driven feed-forward circuit; (ii) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently recognized lymphoid pathways; and (iii) the presence of multiple thresholds and feedback loops, which could significantly affect the propagation of inflammation across multiple body compartments. Future Directions: These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets. Antioxid. Redox Signal. 23, 1370?1387.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140310/1/ars.2015.6398.pd

Modeling The Spatiotemporal Dynamics Of Cells In The Lung

Multiple research problems related to the lung involve a need to take into account the spatiotemporal dynamics of the underlying component cells. Two such problems involve better understanding the nature of the allergic inflammatory response to explore what might cause chronic inflammatory diseases such as asthma, and determining the rules underlying stem cells used to engraft decellularized lung scaffolds in the hopes of growing new lungs for transplantation. For both problems, we model the systems computationally using agent-based modeling, a tool that enables us to capture these spatiotemporal dynamics by modeling any biological system as a collection of agents (cells) interacting with each other and within their environment. This allows to test the most important pieces of biological systems together rather than in isolation, and thus rapidly derive biological insights from resulting complex behavior that could not have been predicted beforehand, which we can then use to guide wet lab experimentation. For the allergic response, we hypothesized that stimulation of the allergic response with antigen results in a response with formal similarity to a muscle twitch or an action potential, with an inflammatory phase followed by a resolution phase that returns the system to baseline. We prepared an agent-based model (ABM) of the allergic inflammatory response and determined that antigen stimulation indeed results in a twitch-like response. To determine what might cause chronic inflammatory diseases where the twitch presumably cannot resolve back to baseline, we then tested multiple potential defects to the model. We observed that while most of these potential changes lessen the magnitude of the response but do not affect its overall behavior, extending the lifespan of activated pro-inflammatory cells such as neutrophils and eosinophil results in a prolonged inflammatory response that does not resolve to baseline. Finally, we performed a series of experiments involving continual antigen stimulation in mice, determining that there is evidence in the cytokine, cellular and physiologic (mechanical) response consistent with our hypothesis of a finite twitch and an associated refractory period. For stem cells, we made a 3-D ABM of a decellularized scaffold section seeded with a generic stem cell type. We then programmed in different sets of rules that could conceivably underlie the cell\u27s behavior, and observed the change in engraftment patterns in the scaffold over selected timepoints. We compared the change in those patterns against the change in experimental scaffold images seeded with C10 epithelial cells and mesenchymal stem cells, two cell types whose behaviors are not well understood, in order to determine which rulesets more closely match each cell type. Our model indicates that C10s are more likely to survive on regions of higher substrate while MSCs are more likely to proliferate on regions of higher substrate

A Computational, Tissue-Realistic Model of Pressure Ulcer Formation in Individuals with Spinal Cord Injury

People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to “better” vs. “worse” outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU

A three constituent mixture theory model of cutaneous and subcutaneous tissue in the context of neonatal pressure ulcer etiology and prevention

Localized ischemia, impaired interstitial fluid flow, and sustained mechanical loading of cells have all been hypothesized as mechanisms of pressure ulcer (PrU) etiology. Time-varying loading has experimentally been shown to increase fluid flow in human skin in vivo. Towards the design of prophylactic protocols and treatment modalities for PrU management there is a need for an analytical model to investigate the local fluid flow characteristics of skin tissue under time-varying loading. In this study, a triphasic mixture theory model with constituents of extracellular matrix, interstitial fluid, and blood was calibrated and validated and used to investigate stress and fluid velocity under quasi-static and time-varying loading conditions, respectively. Four input strain profiles were considered, including uniform, geometric circular segment, Gaussian, and Hertz-type strain profiles. Calibrated bulk and shear modulus (κ;=227.7kPa, µ=1.04kPa) were on the same order of magnitude as literature. Fluid velocities were investigated for apparent strain amplitudes of 100-700μϵ and frequencies of 10-80Hz. At the lowest amplitude and frequency, interstitial fluid velocities were on the same order of magnitude as literature values, 1 micrometers/s and 1 mm/s, respectively. Interstitial fluid and blood velocity both experienced significant increases with increasing amplitude and frequency. The study demonstrated the ability to analytically predict quasi-static stress profiles as well as predict fluid velocity increases in cyclically loaded soft tissues by employing quasi-static mechanics and mixture theory models. Consequently, this study builds a strong foundation for use in the development of vibrational support surfaces for use in prophylactic protocols and adjunctive treatment modalities for PrU managemen

Methods in Computational Biology

Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measuremen

Understanding the Role of TGF-b1 in Interstitial Lung Disease Using A Multi-Scale Systems Biology Approach

Pulmonary diseases are a major global health burden affecting approximately one billion people every year. They result from many types of insults including but not limited to infections, such as tuberculosis (TB), and dysregulations of the lung physiology, such as idiopathic pulmonary fibrosis. (IPF) The key to producing better therapeutics to treat pulmonary diseases is in understanding the role of immune mediators in these diseases. Transforming growth factor-β1 (TGF-β1) is an immune mediator that has been implicated in the exacerbation of both TB and IPF. TGF-β1 is traditionally described as and anti-inflammatory cytokine, thought to restrict the immune response. TGF-β1 affects a variety of cellular processes including proliferation, cytokine secretion, and even apoptosis. These effects are very cell type specific and often concentration dependent. Effective modulation of the immune response through TGF-β1 requires understanding which cells are being regulated, what are the specific results of TGF-β1 regulation, and through what mechanisms TGF-β1 is acting on the cells. To answer these questions it is necessary to look across biological scales at TGF-β1 signaling on a molecular scale, a cellular scale, and a tissue scale. The role of TGF-β1 across multiple biologic scales has not been well characterized in the context of pulmonary disease. In this work I took a multi-scale systems biology approach to understanding the mechanistic role of TGF-β1 in pulmonary disease across molecular, cellular, and tissue scales. I constructed a novel ordinary differential equation (ODE) model of TGF-β1 receptor ligand signaling in a single fibroblast and from that model, identified the necessity for simultaneous TGF-β1 and prostaglandin E2 signaling to maintain homeostatic fibroblast response during injury. I then combined this ODE model with a novel in silico agent based model (ABM) of fibroblasts and epithelial cells in co-culture in order to evaluate the effects of molecular scale signaling dynamics of cellular scale outputs such as cell proliferation, differentiation, and survival. With this model I identified a need for differential therapeutic treatment of fibroblasts and epithelial cells in order to prevent exacerbation of fibrotic disease. I then introduced TGF-β1 signaling into the existing in silico ABM model of TB induced granuloma formation in the lung (GranSim). Using this updated version of GranSim in combination with studies performed in non-human primates, I demonstrate the inhibition of TGF-β1 in the granuloma increases bacterial killing and promotes lesion sterilization by enabling increased effector functions from cytotoxic T cells. I also show that macrophages and cytotoxic T cells are differentially regulated in the granuloma by interleukin-10 and TGF-β1 respectively. Finally, I combine work on fibrosis and granuloma formation by introducing fibroblasts with an ODE model defining TGF-β1 receptor-ligand signaling dynamics into GranSim in order to characterize the formation of fibrotic granulomas. In this work I have advanced the understanding of TGF-β1 regulation in pulmonary disease and opened doors for further examination of potential therapeutic targets to treat these diseases.PHDMicrobiology & ImmunologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137147/1/warsinhc_1.pd