A new decision sciences for complex systems

Models of complex systems can capture much useful information but can be difficult to apply to real-world decision-making because the type of information they contain is often inconsistent with that required for traditional decision analysis. New approaches, which use inductive reasoning over large ensembles of computational experiments, now make possible systematic comparison of alternative policy options using models of complex systems. This article describes Computer-Assisted Reasoning, an approach to decision-making under conditions of deep uncertainty that is ideally suited to applying complex systems to policy analysis. The article demonstrates the approach on the policy problem of global climate change, with a particular focus on the role of technology policies in a robust, adaptive strategy for greenhouse gas abatement.

Twenty-first century approaches to ancient problems: Climate and society

By documenting how humans adapted to changes in their environment that are often much greater than those experienced in the instrumental record, archaeology provides our only deep-time laboratory for highlighting the circumstances under which humans managed or failed to find to adaptive solutions to changing climate, not just over a few generations but over the longue durée Patterning between climate-mediated environmental change and change in human societies has, however, been murky because of low spatial and temporal resolution in available datasets, and because of failure to model the effects of climate change on local resources important to human societies. In this paper we review recent advances in computational modeling that, in conjunction with improving data, address these limitations. These advances include network analysis, niche and species distribution modeling, and agent-based modeling. These studies demonstrate the utility of deep-time modeling for calibrating our understanding of how climate is influencing societies today and may in the future.

Social networks and smoking: exploring the effects of peer influence and smoker popularity through simulations

Adolescent smoking and friendship networks are related in many ways that can amplify smoking prevalence. Understanding and developing interventions within such a complex system requires new analytic approaches. We draw on recent advances in dynamic network modeling to develop a technique that explores the implications of various intervention strategies targeted toward micro-level processes. Our approach begins by estimating a stochastic actor-based model using data from one school in the National Longitudinal Study of Adolescent Health. The model provides estimates of several factors predicting friendship ties and smoking behavior. We then use estimated model parameters to simulate the coevolution of friendship and smoking behavior under potential intervention scenarios. Namely, we manipulate the strength of peer influence on smoking and the popularity of smokers relative to nonsmokers. We measure how these manipulations affect smoking prevalence, smoking initiation, and smoking cessation. Results indicate that both peer influence and smoking-based popularity affect smoking behavior and that their joint effects are nonlinear. This study demonstrates how a simulation-based approach can be used to explore alternative scenarios that may be achievable through intervention efforts and offers new hypotheses about the association between friendship and smoking.

A model of social influence on body mass index

In this paper, we develop an agent-based model of social influence on body weight. The model's assumptions are grounded in theory and evidence from physiology, social psychology, and behavioral science, and its outcomes are tested against longitudinal data from American youth. We discuss the implementation of the model, the insights it generates, and its implications for public health policy. By explicating a well-grounded dynamic mechanism, our analysis helps clarify important dependencies for both efforts to leverage social influence for obesity intervention and efforts to interpret clustering of BMI in networks.

Deletion of TGF-β1 Increases Bacterial Clearance by Cytotoxic T Cells in a Tuberculosis Granuloma Model

Mycobacterium tuberculosis is the pathogenic bacterium that causes tuberculosis (TB), one of the most lethal infectious diseases in the world. The only vaccine against TB is minimally protective, and multi-drug resistant TB necessitates new therapeutics to treat infection. Developing new therapies requires a better understanding of the complex host immune response to infection, including dissecting the processes leading to formation of granulomas, the dense cellular lesions associated with TB. In this work, we pair experimental and computational modeling studies to explore cytokine regulation in the context of TB. We use our next-generation hybrid multi-scale model of granuloma formation (GranSim) to capture molecular, cellular, and tissue scale dynamics of granuloma formation. We identify TGF-β1 as a major inhibitor of cytotoxic T-cell effector function in granulomas. Deletion of TGF-β1 from the system results in improved bacterial clearance and lesion sterilization. We also identify a novel dichotomous regulation of cytotoxic T cells and macrophages by TGF-β1 and IL-10, respectively. These findings suggest that increasing cytotoxic T-cell effector functions may increase bacterial clearance in granulomas and highlight potential new therapeutic targets for treating TB.

Systems biology beyond networks: generating order from disorder through self-organization

Erwin Schrödinger pointed out in his 1944 book "What is Life" that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated.

On agent-based modeling and computational social science

In the first part of the paper, the field of agent-based modeling (ABM) is discussed focusing on the role of generative theories, aiming at explaining phenomena by growing them. After a brief analysis of the major strengths of the field some crucial weaknesses are analyzed. In particular, the generative power of ABM is found to have been underexploited, as the pressure for simple recipes has prevailed and shadowed the application of rich cognitive models. In the second part of the paper, the renewal of interest for Computational Social Science (CSS) is focused upon, and several of its variants, such as deductive, generative, and complex CSS, are identified and described. In the concluding remarks, an interdisciplinary variant, which takes after ABM, reconciling it with the quantitative one, is proposed as a fundamental requirement for a new program of the CSS.

Spatial simulations in systems biology: from molecules to cells

Cells are highly organized objects containing millions of molecules. Each biomolecule has a specific shape in order to interact with others in the complex machinery. Spatial dynamics emerge in this system on length and time scales which can not yet be modeled with full atomic detail. This review gives an overview of methods which can be used to simulate the complete cell at least with molecular detail, especially Brownian dynamics simulations. Such simulations require correct implementation of the diffusion-controlled reaction scheme occurring on this level. Implementations and applications of spatial simulations are presented, and finally it is discussed how the atomic level can be included for instance in multi-scale simulation methods.

Interventions to Reduce the Incidence of Hospital-Onset Clostridium difficile Infection: An Agent-Based Modeling Approach to Evaluate Clinical Effectiveness in Adult Acute Care Hospitals

Background

Despite intensified efforts to reduce hospital-onset Clostridium difficile infection (HO-CDI), its clinical and economic impacts continue to worsen. Many institutions have adopted bundled interventions that vary considerably in composition, strength of evidence, and effectiveness. Considerable gaps remain in our knowledge of intervention effectiveness and disease transmission, which hinders HO-CDI prevention.

Methods

We developed an agent-based model of C. difficile transmission in a 200-bed adult hospital using studies from the literature, supplemented with primary data collection. The model includes an environmental component and 4 distinct agent types: patients, visitors, nurses, and physicians. We used the model to evaluate the comparative clinical effectiveness of 9 single interventions and 8 multiple-intervention bundles at reducing HO-CDI and asymptomatic C. difficile colonization.

Results

Daily cleaning with sporicidal disinfectant and C. difficile screening at admission were the most effective single-intervention strategies, reducing HO-CDI by 68.9% and 35.7%, respectively (both P < .001). Combining these interventions into a 2-intervention bundle reduced HO-CDI by 82.3% and asymptomatic hospital-onset colonization by 90.6% (both, P < .001). Adding patient hand hygiene to healthcare worker hand hygiene reduced HO-CDI rates an additional 7.9%. Visitor hand hygiene and contact precaution interventions did not reduce HO-CDI, compared with baseline. Excluding those strategies, healthcare worker contact precautions were the least effective intervention at reducing hospital-onset colonization and infection.

Conclusions

Identifying and managing the vast hospital reservoir of asymptomatic C. difficile by screening and daily cleaning with sporicidal disinfectant are high-yield strategies. These findings provide much-needed data regarding which interventions to prioritize for optimal C. difficile control.

A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission

The Global-Scale Agent Model (GSAM) is presented. The GSAM is a high-performance distributed platform for agent-based epidemic modeling capable of simulating a disease outbreak in a population of several billion agents. It is unprecedented in its scale, its speed, and its use of Java. Solutions to multiple challenges inherent in distributing massive agent-based models are presented. Communication, synchronization, and memory usage are among the topics covered in detail. The memory usage discussion is Java specific. However, the communication and synchronization discussions apply broadly. We provide benchmarks illustrating the GSAM's speed and scalability.

Toward a multi-scale computational model of arterial adaptation in hypertension: verification of a multi-cell agent based model

Agent-based models (ABMs) represent a novel approach to study and simulate complex mechano chemo-biological responses at the cellular level. Such models have been used to simulate a variety of emergent responses in the vasculature, including angiogenesis and vasculogenesis. Although not used previously to study large vessel adaptations, we submit that ABMs will prove equally useful in such studies when combined with well-established continuum models to form multi-scale models of tissue-level phenomena. In order to couple agent-based and continuum models, however, there is a need to ensure that each model faithfully represents the best data available at the relevant scale and that there is consistency between models under baseline conditions. Toward this end, we describe the development and verification of an ABM of endothelial and smooth muscle cell responses to mechanical stimuli in a large artery. A refined rule-set is proposed based on a broad literature search, a new scoring system for assigning confidence in the rules, and a parameter sensitivity study. To illustrate the utility of these new methods for rule selection, as well as the consistency achieved with continuum-level models, we simulate the behavior of a mouse aorta during homeostasis and in response to both transient and sustained increases in pressure. The simulated responses depend on the altered cellular production of seven key mitogenic, synthetic, and proteolytic biomolecules, which in turn control the turnover of intramural cells and extracellular matrix. These events are responsible for gross changes in vessel wall morphology. This new ABM is shown to be appropriately stable under homeostatic conditions, insensitive to transient elevations in blood pressure, and responsive to increased intramural wall stress in hypertension.

Rule-Based Simulation of Multi-Cellular Biological Systems-A Review of Modeling Techniques

Emergent behaviors of multi-cellular biological systems (MCBS) result from the behaviors of each individual cells and their interactions with other cells and with the environment. Modeling MCBS requires incorporating these complex interactions among the individual cells and the environment. Modeling approaches for MCBS can be grouped into two categories: continuum models and cell-based models. Continuum models usually take the form of partial differential equations, and the model equations provide insight into the relationship among the components in the system. Cell-based models simulate each individual cell behavior and interactions among them enabling the observation of the emergent system behavior. This review focuses on the cell-based models of MCBS, and especially, the technical aspect of the rule-based simulation method for MCBS is reviewed. How to implement the cell behaviors and the interactions with other cells and with the environment into the computational domain is discussed. The cell behaviors reviewed in this paper are division, migration, apoptosis/necrosis, and differentiation. The environmental factors such as extracellular matrix, chemicals, microvasculature, and forces are also discussed. Application examples of these cell behaviors and interactions are presented.

Extracellular DNA and Type IV Pilus Expression Regulate the Structure and Kinetics of Biofilm Formation by Nontypeable Haemophilus influenzae

Biofilms formed in the middle ear by nontypeable Haemophilus influenzae (NTHI) are central to the chronicity, recurrence, and refractive nature of otitis media (OM). However, mechanisms that underlie the emergence of specific NTHI biofilm structures are unclear. We combined computational analysis tools and in silico modeling rooted in statistical physics with confocal imaging of NTHI biofilms formed in vitro during static culture in order to identify mechanisms that give rise to distinguishing morphological features. Our analysis of confocal images of biofilms formed by NTHI strain 86-028NP using pair correlations of local bacterial densities within sequential planes parallel to the substrate showed the presence of fractal structures of short length scales (≤10 μm). The in silico modeling revealed that extracellular DNA (eDNA) and type IV pilus (Tfp) expression played important roles in giving rise to the fractal structures and allowed us to predict a substantial reduction of these structures for an isogenic mutant (ΔcomE) that was significantly compromised in its ability to release eDNA into the biofilm matrix and had impaired Tfp function. This prediction was confirmed by analysis of confocal images of in vitro ΔcomE strain biofilms. The fractal structures potentially generate niches for NTHI survival in the hostile middle ear microenvironment by dramatically increasing the contact area of the biofilm with the surrounding environment, facilitating nutrient exchange, and by generating spatial positive feedback to quorum signaling.

NTHI is a major bacterial pathogen for OM, which is a common ear infection in children worldwide. Chronic OM is associated with bacterial biofilm formation in the middle ear; therefore, knowledge of the mechanisms that underlie NTHI biofilm formation is important for the development of therapeutic strategies for NTHI-associated OM. Our combined approach using confocal imaging of NTHI biofilms formed in vitro and mathematical tools for analysis of pairwise density correlations and agent-based modeling revealed that eDNA and Tfp expression were important factors in the development of fractal structures in NTHI biofilms. These structures may help NTHI survive in hostile environments, such as the middle ear. Our in silico model can be used in combination with laboratory or animal modeling studies to further define the mechanisms that underlie NTHI biofilm development during OM and thereby guide the rational design of, and optimize time and cost for, benchwork and preclinical studies.

Integrating Complex Systems Methods to Advance Obesity Prevention Intervention Research

Background. Whole-of-community interventions have been recommended and show promise for preventing obesity; however, research to understand the mechanisms underlying their success or failure is lacking. Complex systems approaches may be useful to address this gap. Purpose. To describe the evolution and utilization of qualitative and quantitative complex systems methods to understand and model whole-of-community obesity prevention interventions. Approach. We illustrate the retrospective qualitative development of a systems map representing community change dynamic within the Shape Up Somerville (SUS) intervention. We then describe how this systems map, and complementary work of other successful obesity prevention interventions (Romp & Chomp intervention), informed the COMPACT (childhood obesity modeling for prevention and community transformation) study. COMPACT's design aligns complex systems science principles and community-engaged research to better understand stakeholders' leadership roles in whole-of-community interventions. We provide an overview of the complex systems tools used in COMPACT: agent-based modeling, group model building, and social network analysis and describe how whole-of-community intervention stakeholders ("agents") use their social networks to diffuse knowledge about and engagement with childhood obesity prevention efforts, laying the groundwork for community readiness for sustainable change. Conclusion. Complex systems approaches appear feasible and useful to study whole-of-community obesity prevention interventions and provide novel insights that expand on those gained from traditional approaches. Use of multiple methods, both qualitative and quantitative, from the complex systems toolkit working together can be important to success.

From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality

Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality) is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression), stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s) underlying it for the specific microbial system and question investigated is essential for selecting the optimal modeling strategy.

Assessing the impacts of local knowledge and technology on climate change vulnerability in remote communities

The introduction of new technologies into small remote communities can alter how individuals acquire knowledge about their surrounding environment. This is especially true when technologies that satisfy basic needs, such as freshwater use, create a distance (i.e., diminishing exposure) between individuals and their environment. However, such distancing can potentially be countered by the transfer of local knowledge between community members and from one generation to the next. The objective of this study is to simulate by way of agent-based modeling the tensions between technology-induced distancing and local knowledge that are exerted on community vulnerability to climate change. A model is developed that simulates how a collection of individual perceptions about changes to climatic-related variables manifest into community perceptions, how perceptions are influenced by the movement away from traditional resource use, and how the transmission of knowledge mitigates the potentially adverse effects of technology-induced distancing. The model is implemented utilizing climate and social data for two remote communities located on the Seward Peninsula in western Alaska. The agent-based model simulates a set of scenarios that depict different ways in which these communities may potentially engage with their natural resources, utilize knowledge transfer, and develop perceptions of how the local climate is different from previous years. A loosely-coupled pan-arctic climate model simulates changes monthly changes to climatic variables. The discrepancy between the perceptions derived from the agent-based model and the projections simulated by the climate model represent community vulnerability. The results demonstrate how demographics, the communication of knowledge and the types of ‘knowledge-providers’ influence community perception about changes to their local climate.

Agent-Based Modeling in Molecular Systems Biology

Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.

Modeling social norms and social influence in obesity

The worldwide increase in obesity has led to changes in what is considered "normal" or desirable weight, especially among populations at higher risk. We show that social norms are key to understanding the obesity epidemic, and that social influence mechanisms provide a necessary linkage between individual obesity-related behaviors and population-level characteristics. Because influence mechanisms cannot be directly observed, we show how three complex systems tools may be used to gain insights into observed epidemiologic patterns: social network analysis, agent-based modeling, and systems dynamics modeling. However, simulation and mathematical modeling approaches raise questions regarding acceptance of findings, especially among policy makers. Nevertheless, we point to modeling successes in obesity and other fields, including the NIH-funded National Collaborative on Childhood Obesity Research (NCCOR) Envison project.

Implementation of Syringe Services Programs to Prevent Rapid Human Immunodeficiency Virus Transmission in Rural Counties in the United States: A Modeling Study

BACKGROUND

Syringe services programs (SSPs) are effective venues for delivering harm-reduction services to people who inject drugs (PWID). However, SSPs often face significant barriers to implementation, particularly in the absence of known human immunodeficiency virus (HIV) outbreaks.

METHODS

Using an agent-based model, we simulated HIV transmission in Scott County, Indiana, a rural county with a 1.7% prevalence of injection drug use. We compared outcomes arising in the absence of an SSP, in the presence of a pre-existing SSP, and with implementation of an SSP after the detection of an HIV outbreak among PWID over 5 years following the introduction of a single infection into the network.

RESULTS

In the absence of an SSP, the model predicted an average of 176 infections among PWID over 5 years or an incidence rate of 12.1/100 person-years. Proactive implementation averted 154 infections and decreased incidence by 90.3%. With reactive implementation beginning operations 10 months after the first infection, an SSP would prevent 107 infections and decrease incidence by 60.8%. Reductions in incidence were also observed among people who did not inject drugs.

CONCLUSIONS

Based on model predictions, proactive implementation of an SSP in Scott County had the potential to avert more HIV infections than reactive implementation after the detection of an outbreak. The predicted impact of reactive SSP implementation was highly dependent on timely implementation after detecting the earliest infections. Consequently, there is a need for expanded proactive SSP implementation in the context of enhanced monitoring of outbreak vulnerability in Scott County and similar rural contexts.

A systematic review of system dynamics and agent-based obesity models: Evaluating obesity as part of the global syndemic

The problem of obesity has recently been reframed as part of the global syndemic-the co-occurring, interacting pandemics of obesity, undernutrition, and climate change that are driven by common underlying societal drivers. System science modeling approaches may help clarify how these shared drivers operate and the best ways to address them. The objective of this paper was to determine to what extent existing agent-based and system dynamics computational models of obesity provide insights into the shared drivers of the global syndemic. Peer-reviewed studies published until July 2018 were identified from Scopus, Web of Science, and PubMed databases. Thirty-eight studies representing 30 computational models were included. They show a growing use of system dynamics and agent-based modeling in the past decade. They most often examined mechanisms and interventions in the areas of social network-based influences on obesity, physiology and disease state mechanics, and the role of food and physical activity environments. Usefulness for identifying common drivers of the global syndemic was mixed; most models represented Western settings and focused on obesity determinants close to the person (eg, social circles, school settings, and neighborhood environments), with a relative paucity in models at mesolevel and macrolevel and in developing country contexts.

Analysis of Intelligent Transportation Systems Using Model-Driven Simulations

Intelligent Transportation Systems (ITSs) integrate information, sensor, control, and communication technologies to provide transport related services. Their users range from everyday commuters to policy makers and urban planners. Given the complexity of these systems and their environment, their study in real settings is frequently unfeasible. Simulations help to address this problem, but present their own issues: there can be unintended mistakes in the transition from models to code; their platforms frequently bias modeling; and it is difficult to compare works that use different models and tools. In order to overcome these problems, this paper proposes a framework for a model-driven development of these simulations. It is based on a specific modeling language that supports the integrated specification of the multiple facets of an ITS: people, their vehicles, and the external environment; and a network of sensors and actuators conveniently arranged and distributed that operates over them. The framework works with a model editor to generate specifications compliant with that language, and a code generator to produce code from them using platform specifications. There are also guidelines to help researchers in the application of this infrastructure. A case study on advanced management of traffic lights with cameras illustrates its use.

Agent-based modeling approach of immune defense against spores of opportunistic human pathogenic fungi

Opportunistic human pathogenic fungi like the ubiquitous fungus Aspergillus fumigatus are a major threat to immunocompromised patients. An impaired immune system renders the body vulnerable to invasive mycoses that often lead to the death of the patient. While the number of immunocompromised patients is rising with medical progress, the process, and dynamics of defense against invaded and ready to germinate fungal conidia are still insufficiently understood. Besides macrophages, neutrophil granulocytes form an important line of defense in that they clear conidia. Live imaging shows the interaction of those phagocytes and conidia as a dynamic process of touching, dragging, and phagocytosis. To unravel strategies of phagocytes on the hunt for conidia an agent-based modeling approach is used, implemented in NetLogo. Different modes of movement of phagocytes are tested regarding their clearing efficiency: random walk, short-term persistence in their recent direction, chemotaxis of chemokines excreted by conidia, and communication between phagocytes. While the short-term persistence hunting strategy turned out to be superior to the simple random walk, following a gradient of chemokines released by conidial agents is even better. The advantage of communication between neutrophilic agents showed a strong dependency on the spatial scale of the focused area and the distribution of the pathogens.

Bystander Effects of Hypoxia-Activated Prodrugs: Agent-Based Modeling Using Three Dimensional Cell Cultures

Intra-tumor heterogeneity represents a major barrier to anti-cancer therapies. One strategy to minimize this limitation relies on bystander effects via diffusion of cytotoxins from targeted cells. Hypoxia-activated prodrugs (HAPs) have the potential to exploit hypoxia in this way, but robust methods for measuring bystander effects are lacking. The objective of this study is to develop experimental models (monolayer, multilayer, and multicellular spheroid co-cultures) comprising 'activator' cells with high expression of prodrug-activating reductases and reductase-deficient 'target' cells, and to couple these with agent-based models (ABMs) that describe diffusion and reaction of prodrugs and their active metabolites, and killing probability for each cell. HCT116 cells were engineered as activators by overexpressing P450 oxidoreductase (POR) and as targets by knockout of POR, with fluorescent protein and antibiotic resistance markers to enable their quantitation in co-cultures. We investigated two HAPs with very different pharmacology: SN30000 is metabolized to DNA-breaking free radicals under hypoxia, while the dinitrobenzamide PR104A generates DNA-crosslinking nitrogen mustard metabolites. In anoxic spheroid co-cultures, increasing the proportion of activator cells decreased killing of both activators and targets by SN30000. An ABM parameterized by measuring SN30000 cytotoxicity in monolayers and diffusion-reaction in multilayers accurately predicted SN30000 activity in spheroids, demonstrating the lack of bystander effects and that rapid metabolic consumption of SN30000 inhibited prodrug penetration. In contrast, killing of targets by PR104A in anoxic spheroids was markedly increased by activators, demonstrating that a bystander effect more than compensates any penetration limitation. However, the ABM based on the well-studied hydroxylamine and amine metabolites of PR104A did not fit the cell survival data, indicating a need to reassess its cellular pharmacology. Characterization of extracellular metabolites of PR104A in anoxic cultures identified more stable, lipophilic, activated dichloro mustards with greater tissue diffusion distances. Including these metabolites explicitly in the ABM provided a good description of activator and target cell killing by PR104A in spheroids. This study represents the most direct demonstration of a hypoxic bystander effect for PR104A to date, and demonstrates the power of combining mathematical modeling of pharmacokinetics/pharmacodynamics with multicellular culture models to dissect bystander effects of targeted drug carriers.

Modeling social dimensions of oral health among older adults in urban environments

In both developed and developing countries, population aging has attained unprecedented levels. Public health strategies to deliver services in community-based settings are key to enhancing the utilization of preventive care and reducing costs for this segment of the population. Motivated by concerns of inadequate access to oral health care by older adults in urban environments, this article presents a portfolio of systems science models that have been developed on the basis of observations from the ElderSmile preventive screening program operated in northern Manhattan, New York City, by the Columbia University College of Dental Medicine. Using the methodology of system dynamics, models are developed to explore how interpersonal relationships influence older adults' participation in oral health promotion. Feedback mechanisms involving word of mouth about preventive screening opportunities are represented in relation to stocks that change continuously via flows, as well as agents whose states of health care utilization change discretely using stochastic transitions. Agent-based implementations illustrate how social networks and geographic information systems are integrated into dynamic models to reflect heterogeneous and proximity-based patterns of communication and participation in the ElderSmile program. The systems science approach builds shared knowledge among an interdisciplinary research team about the dynamics of access to opportunities for oral health promotion. Using "what if" scenarios to model the effects of program enhancements and policy changes, resources may be effectively leveraged to improve access to preventive and treatment services. Furthermore, since oral health and general health are inextricably linked, the integration of services may improve outcomes and lower costs.

Computational Modeling Predicts Simultaneous Targeting of Fibroblasts and Epithelial Cells Is Necessary for Treatment of Pulmonary Fibrosis

Pulmonary fibrosis is pathologic remodeling of lung tissue that can result in difficulty breathing, reduced quality of life, and a poor prognosis for patients. Fibrosis occurs as a result of insult to lung tissue, though mechanisms of this response are not well-characterized. The disease is driven in part by dysregulation of fibroblast proliferation and differentiation into myofibroblast cells, as well as pro-fibrotic mediator-driven epithelial cell apoptosis. The most well-characterized pro-fibrotic mediator associated with pulmonary fibrosis is TGF-β1. Excessive synthesis of, and sensitivity to, pro-fibrotic mediators as well as insufficient production of and sensitivity to anti-fibrotic mediators has been credited with enabling fibroblast accumulation. Available treatments neither halt nor reverse lung damage. In this study we have two aims: to identify molecular and cellular scale mechanisms driving fibroblast proliferation and differentiation as well as epithelial cell survival in the context of fibrosis, and to predict therapeutic targets and strategies. We combine in vitro studies with a multi-scale hybrid agent-based computational model that describes fibroblasts and epithelial cells in co-culture. Within this model TGF-β1 represents a pro-fibrotic mediator and we include detailed dynamics of TGF-β1 receptor ligand signaling in fibroblasts. PGE2 represents an anti-fibrotic mediator. Using uncertainty and sensitivity analysis we identify TGF-β1 synthesis, TGF-β1 activation, and PGE2 synthesis among the key mechanisms contributing to fibrotic outcomes. We further demonstrate that intervention strategies combining potential therapeutics targeting both fibroblast regulation and epithelial cell survival can promote healthy tissue repair better than individual strategies. Combinations of existing drugs and compounds may provide significant improvements to the current standard of care for pulmonary fibrosis. Thus, a two-hit therapeutic intervention strategy may prove necessary to halt and reverse disease dynamics.