Investigating biocomplexity through the agent-based paradigm

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. Here, we give a background on recent advances in research on brain-immune system cross-talk in Alzheimer's disease and review machine learning approaches, which can combine multiple biomarkers with further information (e.g. age, sex, APOE genotype) into predictive models supporting an earlier diagnosis. In addition, mechanistic modeling approaches, such as agent-based modeling open the possibility to model and analyze cell dynamics over time. This review aims to provide an overview of the current state of immune-system related blood-based biomarkers and their potential for the early diagnosis of Alzheimer's disease.

Virtual cells in a virtual microenvironment recapitulate early development-like patterns in human pluripotent stem cell colonies

The mechanism by which morphogenetic signals engage the regulatory networks responsible for early embryonic tissue patterning is incompletely understood. Here, we developed a minimal gene regulatory network (GRN) model of human pluripotent stem cell (hPSC) lineage commitment and embedded it into "cellular" agents that respond to a dynamic morphogenetic signaling microenvironment. Simulations demonstrated that GRN wiring had significant non-intuitive effects on tissue pattern order, composition, and dynamics. Experimental perturbation of GRN connectivities supported model predictions and demonstrated the role of OCT4 as a master regulator of peri-gastrulation fates. Our so-called GARMEN strategy provides a multiscale computational platform to understand how single-cell-based regulatory interactions scale to tissue domains. This foundation provides new opportunities to simulate the impact of network motifs on normal and aberrant tissue development.

An integrative multiscale view of early cardiac looping

The heart is the first organ to form and function during the development of an embryo. Heart development consists of a series of events believed to be highly conserved in vertebrates. Development of heart begins with the formation of the cardiac fields followed by a linear heart tube formation. The straight heart tube then undergoes a ventral bending prior to further bending and helical torsion to form a looped heart. The looping phase is then followed by ballooning, septation, and valve formation giving rise to a four-chambered heart in avians and mammals. The looping phase plays a central role in heart development. Successful looping is essential for proper alignment of the future cardiac chambers and tracts. As aberrant looping results in various congenital heart diseases, the mechanisms of cardiac looping have been studied for several decades by various disciplines. Many groups have studied anatomy, biology, genetics, and mechanical processes during heart looping, and have proposed multiple mechanisms. Computational modeling approaches have been utilized to examine the proposed mechanisms of the looping process. Still, the exact underlying mechanism(s) controlling the looping phase remain poorly understood. Although further experimental measurements are obviously still required, the need for more integrative computational modeling approaches is also apparent in order to make sense of the vast amount of experimental data and the complexity of multiscale developmental systems. Indeed, there needs to be an iterative interaction between experimentation and modeling in order to properly find the gap in the existing data and to validate proposed hypotheses. This article is categorized under: Cardiovascular Diseases > Genetics/Genomics/Epigenetics Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Molecular and Cellular Physiology.

Differentially targeted seeding reveals unique pathological alpha-synuclein propagation patterns

Parkinson's Disease is a progressive neurodegenerative disorder characterized by the intracellular accumulation of insoluble alpha-synuclein aggregates into Lewy bodies and neurites. Increasing evidence indicates that Parkinson's Disease progression results from the spread of pathologic alpha-synuclein through neuronal networks. However, the exact mechanisms underlying the propagation of abnormal proteins in the brain are only partially understood. The objective of this study was first to describe the long-term spatiotemporal distributions of Lewy-related pathology in mice injected with alpha-synuclein preformed fibrils and then to recreate these patterns using a computational model that simulates in silico the spread of pathologic alpha-synuclein. In this study, 87 two-to-three-month-old non-transgenic mice were injected with alpha-synuclein preformed fibrils to generate a comprehensive post-mortem dataset representing the long-term spatiotemporal distributions of hyperphosphorylated alpha-synuclein, an established marker of Lewy pathology, across the 426 regions of the Allen Mouse Brain Atlas. The mice were injected into either the caudoputamen, nucleus accumbens or hippocampus and followed over 24 months with pathologic alpha-synuclein quantified at seven intermediate time points. The pathologic patterns observed at each time point in this high-resolution dataset were then compared to those generated using a Susceptible-Infected-Removed computational model, an agent-based model that simulates the spread of pathologic alpha-synuclein for every brain region taking simultaneously into account the effect of regional brain connectivity and Snca gene expression. Our histopathological findings showed that differentially targeted seeding of pathologic alpha-synuclein resulted in unique propagation patterns over 24 months and that most brain regions were permissive to pathology. We found that the Susceptible-Infected-Removed model recreated the observed distributions of pathology over 24 months for each injection site. Null models showed that both Snca gene expression and connectivity had a significant influence on model fit. In sum, our study demonstrates that the combination of normal alpha-synuclein concentration and brain connectomics contributes to making brain regions more vulnerable to the pathological process, providing support for a prion-like spread of pathologic alpha-synuclein. We propose that this rich dataset and the related computational model will help test new hypotheses regarding mechanisms that may alter the spread of pathologic alpha-synuclein in the brain.

The Multiple Dimensions of Networks in Cancer: A Perspective

This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.

Nonlinear dose-time-response functions and health-protective exposure limits for inflammation-mediated diseases

Why have occupational safety regulations in the United States not been more successful in protecting worker health from mesothelioma risks, while apparently succeeding relatively well in reducing silicosis risks? This paper briefly discusses biological bases for thresholds and nonlinearities in exposure-response functions for respirable crystalline silica (RCS) and asbestos, based on modeling a chronic inflammation mode of action (mediated by activation of the NLRP3 inflammasome, for both RCS and asbestos). It applies previously published physiologically based pharmacokinetic (PBPK) models to perform computational experiments illuminating how different time courses of exposure with the same time-weighted average (TWA) concentration affect internal doses in target tissues (lung for RCS and mesothelium for asbestos). Key conclusions are that (i) For RCS, but not asbestos, limiting average (TWA) exposure concentrations also tightly constrains internal doses and ability to trigger chronic inflammation and resulting increases in disease risks (ii) For asbestos, excursions (i.e., spikes in concentrations); and especially the times between them are crucial drivers of internal doses and time until chronic inflammation; and hence (iii) These dynamic aspects of exposure, which are not addressed by current occupational safety regulations, should be constrained to better protect worker health. Adjusting permissible average exposure concentration limits (PELs) and daily excursion limits (ELs) is predicted to have little impact on reducing mesothelioma risks, but increasing the number of days between successive excursions is predicted to be relatively effective in reducing worker risks, even if it has little or no impact on TWA average concentrations.

DP2 antagonism reduces airway smooth muscle mass in asthma by decreasing eosinophilia and myofibroblast recruitment

Increased airway smooth muscle mass, a feature of airway remodeling in asthma, is the strongest predictor of airflow limitation and contributes to asthma-associated morbidity and mortality. No current drug therapy for asthma is known to affect airway smooth muscle mass. Although there is increasing evidence that prostaglandin D₂ type 2 receptor (DP₂) is expressed in airway structural and inflammatory cells, few studies have addressed the expression and function of DP₂ in airway smooth muscle cells. We report that the DP₂ antagonist fevipiprant reduced airway smooth muscle mass in bronchial biopsies from patients with asthma who had participated in a previous randomized placebo-controlled trial. We developed a computational model to capture airway remodeling. Our model predicted that a reduction in airway eosinophilia alone was insufficient to explain the clinically observed decrease in airway smooth muscle mass without a concomitant reduction in the recruitment of airway smooth muscle cells or their precursors to airway smooth muscle bundles that comprise the airway smooth muscle layer. We experimentally confirmed that airway smooth muscle migration could be inhibited in vitro using DP₂-specific antagonists in an airway smooth muscle cell culture model. Our analyses suggest that fevipiprant, through antagonism of DP₂, reduced airway smooth muscle mass in patients with asthma by decreasing airway eosinophilia in concert with reduced recruitment of myofibroblasts and fibrocytes to the airway smooth muscle bundle. Fevipiprant may thus represent a potential therapy to ameliorate airway remodeling in asthma.

Dose-response modeling of NLRP3 inflammasome-mediated diseases: asbestos, lung cancer, and malignant mesothelioma as examples

Can a single fiber of amphibole asbestos increase the risk of lung cancer or malignant mesothelioma (MM)? Traditional linear no-threshold (LNT) risk assessment assumptions imply that the answer is yes: there is no safe exposure level. This paper draws on recent scientific progress in inflammation biology, especially elucidation of the activation thresholds for NLRP3 inflammasomes and resulting chronic inflammation, to model dose-response relationships for malignant mesothelioma and lung cancer risks caused by asbestos exposures. The modeling integrates a physiologically based pharmacokinetics (PBPK) front end with inflammation-driven two-stage clonal expansion (I-TSCE) models of carcinogenesis to describe how exposure leads to chronic inflammation, which in turn promotes carcinogenesis. Together, the combined PBPK and I-TSCE modeling predict that there are practical thresholds for exposure concentration below which asbestos exposure does not cause chronic inflammation in less than a lifetime, and therefore does not increase chronic inflammation-dependent cancer risks. Quantitative examples using model parameter estimates drawn from the literature suggest that practical thresholds may be within about a factor of 2 of some past exposure levels for some workers. The I-TSCE modeling framework explains previous puzzling aspects of asbestos epidemiology, such as why age at first exposure is a better predictor of lifetime MM risk than exposure duration. It may be a valuable tool for risk analysts when LNT assumptions are not justified due to inflammation response thresholds mediating dose-response relationships.

NetLogo agent-based models as tools for understanding the self-organization of cell fate, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium

Interactions between primordium cells and their environment determines the self-organization of the zebrafish posterior Lateral Line primordium as it migrates under the skin from the ear to the tip of the tail forming and depositing neuromasts to spearhead formation of the posterior Lateral Line sensory system. In this review we describe how the NetLogo agent-based programming environment has been used in our lab to visualize and explore how self-generated chemokine gradients determine collective migration, how the dynamics of Wnt signaling can be used to predict patterns of neuromast deposition, and how previously defined interactions between Wnt and Fgf signaling systems have the potential to determine the periodic formation of center-biased Fgf signaling centers in the wake of a shrinking Wnt system. We also describe how NetLogo was used as a database for storing and visualizing the results of in toto lineage analysis of all cells in the migrating primordium. Together, the models illustrate how this programming environment can be used in diverse ways to integrate what has been learnt from biological experiments about the nature of interactions between cells and their environment, and explore how these interactions could potentially determine emergent patterns of cell fate specification, morphogenesis and collective migration of the zebrafish posterior Lateral Line primordium.

Organoids by design

Organoids are multicellular structures that can be derived from adult organs or pluripotent stem cells. Early versions of organoids range from simple epithelial structures to complex, disorganized tissues with large cellular diversity. The current challenge is to engineer cellular complexity into organoids in a controlled manner that results in organized assembly and acquisition of tissue function. These efforts have relied on studies of organ assembly during embryonic development and have resulted in the development of organoids with multilayer tissue complexity and higher-order functions. We discuss how the next generation of organoids can be designed by means of an engineering-based narrative design to control patterning, assembly, morphogenesis, growth, and function.

Respiratory healthcare by design: Computational approaches bringing respiratory precision and personalised medicine closer to bedside

Precision medicine represents a potentially powerful means to alleviate the growing burden of chronic respiratory diseases. To realise its potential, however, we need a systems level understanding of how biological events (signalling pathways, cell-cell interactions, tissue mechanics) integrate across multiple spatial and temporal scales to give rise to pathology. This can be achieved most practically in silico: a paradigm that offers tight control over model parameters and rapid means of testing and generating mechanistic hypotheses. Patient-specific computational models that can enable identification of pathological mechanisms unique to patients' (omics, physiological, and anatomical) profiles and, therefore, personalised drug targets represent a major milestone in precision medicine. Current patient-based models in literature, especially medical devices, cardiac modelling, and respiratory medicine, rely mostly on (partial/ordinary) differential equations and have reached relatively advanced level of maturity. In respiratory medicine, patient-specific simulations mainly include subject scan-based lung mechanics models that can predict pulmonary function, but they treat the (sub)cellular processes as "black-boxes". A recent advance in simulating human airways at a cellular level to make clinical predictions raises the possibility of linking omics and cell level data/models with lung mechanics to understand respiratory pathology at a systems level. This is significant as this approach can be extended to understanding pathologies in other organs as well. Here, I will discuss ways in which computational models have already made contributions to personalised healthcare and how the paradigm can expedite clinical uptake of precision medicine strategies. I will mainly focus on an agent-based, asthmatic virtual patient that predicted the impact of multiple drug pharmacodynamics at the patient level, its potential to develop efficacious precision medicine strategies in respiratory medicine, and the regulatory and ethical challenges accompanying the mainstream application of such models.

A system dynamics modelling simulation based on a cohort of hepatitis B epidemic research in east China community

Hepatitis B constitutes a severe public health challenge in China. The Community-based Collaborative Innovation hepatitis B (CCI-HBV) project is a national epidemiological study of hepatitis B and has been conducting a comprehensive intervention in southern Zhejiang since 2009.The comprehensive intervention in CCI-HBV areas includes the dynamic hepatitis B screening in local residents, the normalised treatment for hepatitis B infections and the upcoming full-aged hepatitis B vaccination. After two rounds of screening (each round taking for 4 years), the initial epidemiological baseline of hepatitis B in Qinggang was obtained, a coastal community in east China. By combining key data and system dynamics modelling, the regional hepatitis B epidemic in 20 years was predicted.There were 1041 HBsAg positive cases out of 12 228 people in Round 1 indicating HBV prevalence of 8.5%. Of the 13 146 people tested in Round 2, 1171 people were HBsAg positive, with a prevalence of 8.9%. By comparing the two rounds of screening, the HBV incidence rate of 0.192 per 100 person-years was observed. By consulting electronic medical records, the HBV onset rate of 0.533 per 100 person-years was obtained. We generated a simulated model to replicate the real-world situation for the next two decades. To evaluate the effect of interventions on regional HBV prevalence, three comparative experiments were conducted.In this study, the regional hepatitis B epidemic in 20 years was predicted and compared with HBV prevalence under different interventions. Owing to the existing challenges in research methodology, this study combined HBV field research and simulation to provide a system dynamics model with close-to-real key data to improve prediction accuracy. The simulation also provided a prompt guidance for the field implementation.

Programming Morphogenesis through Systems and Synthetic Biology

Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids.

BacArena: Individual-based metabolic modeling of heterogeneous microbes in complex communities

Recent advances focusing on the metabolic interactions within and between cellular populations have emphasized the importance of microbial communities for human health. Constraint-based modeling, with flux balance analysis in particular, has been established as a key approach for studying microbial metabolism, whereas individual-based modeling has been commonly used to study complex dynamics between interacting organisms. In this study, we combine both techniques into the R package BacArena (https://cran.r-project.org/package=BacArena) to generate novel biological insights into Pseudomonas aeruginosa biofilm formation as well as a seven species model community of the human gut. For our P. aeruginosa model, we found that cross-feeding of fermentation products cause a spatial differentiation of emerging metabolic phenotypes in the biofilm over time. In the human gut model community, we found that spatial gradients of mucus glycans are important for niche formations which shape the overall community structure. Additionally, we could provide novel hypothesis concerning the metabolic interactions between the microbes. These results demonstrate the importance of spatial and temporal multi-scale modeling approaches such as BacArena.

In nature, organisms are typically found in near proximity to each other, forming symbiotic relationships. Particularly bacteria are often part of highly organized communities such as biofilms. In this study, we integrate the detailed knowledge about the metabolic capabilities of individual organisms into an individual-based modeling approach for simulating the dynamics of local interactions. We provide a fast and flexible framework, in which established computational models for individual organisms can be simulated in communities. Nutrients can diffuse in an area where cells move, divide, and die. The resulting spatial as well as temporal dynamics and metabolic interactions can be analyzed as well as visualized and subsequently compared to experimental findings. We demonstrate how our approach can be used to gain novel insights on dynamics in single species biofilm formation and multi-species intestinal microbial communities.

A Computational Model of Cellular Engraftment on Lung Scaffolds

The possibility that stem cells might be used to regenerate tissue is now being investigated for a variety of organs, but these investigations are still essentially exploratory and have few predictive tools available to guide experimentation. We propose, in this study, that the field of lung tissue regeneration might be better served by predictive tools that treat stem cells as agents that obey certain rules of behavior governed by both their phenotype and their environment. Sufficient knowledge of these rules of behavior would then, in principle, allow lung tissue development to be simulated computationally. Toward this end, we developed a simple agent-based computational model to simulate geographic patterns of cells seeded onto a lung scaffold. Comparison of the simulated patterns to those observed experimentally supports the hypothesis that mesenchymal stem cells proliferate preferentially toward the scaffold boundary, whereas alveolar epithelial cells do not. This demonstrates that a computational model of this type has the potential to assist in the discovery of rules of cellular behavior.

Synergistic activity of polarised osteoblasts inside condensations cause their differentiation

Condensation of pre-osteogenic, or pre-chondrogenic, cells is the first of a series of processes that initiate skeletal development. We present a validated, novel, three-dimensional agent-based model of in vitro intramembranous osteogenic condensation. The model, informed by system heterogeneity and relying on an interaction-reliant strategy, is shown to be sensitive to ‘rules’ capturing condensation growth and can be employed to track activity of individual cells to observe their macroscopic impact. It, therefore, makes available previously inaccessible data, offering new insights and providing a new context for exploring the emergence, as well as normal and abnormal development, of osteogenic structures. Of the several stages of condensation we investigate osteoblast ‘burial’ within the osteoid they deposit. The mechanisms underlying entrapment – required for osteoblasts to differentiate into osteocytes – remain a matter of conjecture with several hypotheses claiming to capture this important transition. Computational examination of this transition indicates that osteoblasts neither turn off nor slow down their matrix secreting genes – a widely held view; nor do they secrete matrix randomly. The model further reveals that osteoblasts display polarised behaviour to deposit osteoid. This is both an important addition to our understanding of condensation and an important validation of the model’s utility.

Agent-based spatiotemporal simulation of biomolecular systems within the open source MASON framework

Agent-based modelling is being used to represent biological systems with increasing frequency and success. This paper presents the implementation of a new tool for biomolecular reaction modelling in the open source Multiagent Simulator of Neighborhoods framework. The rationale behind this new tool is the necessity to describe interactions at the molecular level to be able to grasp emergent and meaningful biological behaviour. We are particularly interested in characterising and quantifying the various effects that facilitate biocatalysis. Enzymes may display high specificity for their substrates and this information is crucial to the engineering and optimisation of bioprocesses. Simulation results demonstrate that molecule distributions, reaction rate parameters, and structural parameters can be adjusted separately in the simulation allowing a comprehensive study of individual effects in the context of realistic cell environments. While higher percentage of collisions with occurrence of reaction increases the affinity of the enzyme to the substrate, a faster reaction (i.e., turnover number) leads to a smaller number of time steps. Slower diffusion rates and molecular crowding (physical hurdles) decrease the collision rate of reactants, hence reducing the reaction rate, as expected. Also, the random distribution of molecules affects the results significantly.

On the genealogy of tissue engineering and regenerative medicine

In this article, we identify and discuss a timeline of historical events and scientific breakthroughs that shaped the principles of tissue engineering and regenerative medicine (TERM). We explore the origins of TERM concepts in myths, their application in the ancient era, their resurgence during Enlightenment, and, finally, their systematic codification into an emerging scientific and technological framework in recent past. The development of computational/mathematical approaches in TERM is also briefly discussed.

Potential new therapeutic modality revealed through agent-based modeling of the neuromuscular junction and acetylcholinesterase inhibition

Background

One of the leading causes of death and illness within the agriculture industry is through unintentionally ingesting or inhaling organophosphate pesticides. OP intoxication directly inhibits acetylcholinesterase, resulting in an excitatory signaling cascade leading to fasciculation, loss of control of bodily fluids, and seizures.

Methods

Our model was developed using a discrete, rules-based modeling approach in NetLogo. This model includes acetylcholinesterase, the nicotinic acetylcholine receptor responsible for signal transduction, a single release of acetylcholine, organophosphate inhibitors, and a theoretical novel medical countermeasure. We have parameterized the system considering the molecular reaction rate constants in an agent-based approach, as opposed to apparent macroscopic rates used in differential equation models.

Results

Our model demonstrates how the cholinergic crisis can be mitigated by therapeutic intervention with an acetylcholinesterase activator. Our model predicts signal rise rates and half-lives consistent with in vitro and in vivo data in the absence and presence of inhibitors. It also predicts the efficacy of theoretical countermeasures acting through three mechanisms: increasing catalytic turnover of acetylcholine, increasing acetylcholine binding affinity to the enzyme, and decreasing binding rates of inhibitors.

Conclusion

We present a model of the neuromuscular junction confirming observed acetylcholine signaling data and suggesting that developing a countermeasure capable of reducing inhibitor binding, and not activator concentration, is the most important parameter for reducing organophosphate (OP) intoxication.

Electronic supplementary material

The online version of this article (doi:10.1186/1742-4682-11-42) contains supplementary material, which is available to authorized users.