Testing,tracing and isolation in compartmental models

Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computational efficiency is such that it can be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks.     

Mechanobiological model of arterial growth and remodeling

A coupled agent-based model (ABM) and finite element analysis (FEA) computational framework is developed to study the interplay of bio-chemo-mechanical factors in blood vessels and their role in maintaining homeostasis. The agent-based model implements the power of REPAST Simphony libraries and adapts its environment for biological simulations. Coupling a continuum-level model (FEA) to a cellular-level model (ABM) has enabled this computational framework to capture the response of blood vessels to increased or decreased levels of growth factors, proteases and other signaling molecules (on the micro scale) as well as altered blood pressure. Performance of the model is assessed by simulating porcine left anterior descending artery under normotensive conditions and transient increases in blood pressure and by analyzing sensitivity of the model to variations in the rule parameters of the ABM. These simulations proved that the model is stable under normotensive conditions and can recover from transient increases in blood pressure. Sensitivity studies revealed that the model is most sensitive to variations in the concentration of growth factors that affect cellular proliferation and regulate extracellular matrix composition (mainly collagen).     

From actors to agents in socio-ecological systems models

The ecosystem service concept has emphasized the role of people within socio-ecological systems (SESs). In this paper, we review and discuss alternative ways of representing people, their behaviour and decision-making processes in SES models using an agent-based modelling (ABM) approach. We also explore how ABM can be empirically grounded using information from social survey. The capacity for ABM to be generalized beyond case studies represents a crucial next step in modelling SESs, although this comes with considerable intellectual challenges. We propose the notion of human functional types, as an analogy of plant functional types, to support the expansion (scaling) of ABM to larger areas. The expansion of scope also implies the need to represent institutional agents in SES models in order to account for alternative governance structures and policy feedbacks. Further development in the coupling of human-environment systems would contribute considerably to better application and use of the ecosystem service concept.     

A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease

Mosquito-borne diseases cause significant public health burden and are widely re-emerging or emerging. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modelling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for individual movement through mosquito habitats, extending the capabilities of existing agent-based models (ABMs) to include vector-borne diseases. The ABM are coupled with differential equations representing ‘clouds’ of mosquitoes in patches accounting for mosquito ecology. We adapted an ABM for humans using this method and investigated the importance of heterogeneity in pathogen spread, motivating the utility of models of individual behaviour. We observed that the final epidemic size is greater in patch models with a high risk patch frequently visited than in a homogeneous model. Our hybrid model quantifies the importance of the heterogeneity in the spread of mosquito-borne pathogens, guiding mitigation strategies.     

Cellulat: an agent-based intracellular signalling model

The theory of behaviour-based systems (or autonomous agents) constitutes a useful approach for the modelling of intracellular signalling networks. In this sense, a cell can be seen as an adaptive autonomous agent or as a society of such agents, where each can exhibit a particular behaviour depending on its cognitive capabilities. We present an intracellular signalling model obtained by integrating several computational techniques into an agent-based paradigm. Cellulat, the model, takes into account two essential aspects of the intracellular signalling networks: (1) cognitive capacities, which are modelled as the agent abilities to interact with the surrounding medium and (2) a spatial organisation, this last obtained using a shared data structure through which the agents communicate between them. We propose a methodology for the modelling of intracellular signalling pathway using Cellulat and we discuss the goal of a virtual laboratory based on our model and presently under development.     

BIO-LGCA: A cellular automaton modelling class for analysing collective cell migration

Collective dynamics in multicellular systems such as biological organs and tissues plays a key role in biological development, regeneration, and pathological conditions. Collective tissue dynamics—understood as population behaviour arising from the interplay of the constituting discrete cells—can be studied with on- and off-lattice agent-based models. However, classical on-lattice agent-based models, also known as cellular automata, fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. To overcome drawbacks of classical on-lattice models, we introduce an on-lattice, agent-based modelling class for collective cell migration, which we call biological lattice-gas cellular automaton (BIO-LGCA). The BIO-LGCA is characterised by synchronous time updates, and the explicit consideration of individual cell velocities. While rules in classical cellular automata are typically chosen ad hoc, rules for cell-cell and cell-environment interactions in the BIO-LGCA can also be derived from experimental cell migration data or biophysical laws for individual cell migration. We introduce elementary BIO-LGCA models of fundamental cell interactions, which may be combined in a modular fashion to model complex multicellular phenomena. We exemplify the mathematical mean-field analysis of specific BIO-LGCA models, which allows to explain collective behaviour. The first example predicts the formation of clusters in adhesively interacting cells. The second example is based on a novel BIO-LGCA combining adhesive interactions and alignment. For this model, our analysis clarifies the nature of the recently discovered invasion plasticity of breast cancer cells in heterogeneous environments.     

An in silico platform for the study of epithelial pre-invasive neoplastic development

To study the possible mechanisms of epithelial preneoplastic development we developed an integrated platform using a 3D agent-based model (ABM). This platform, named idefics, allows for the implementation of normal biological rules at the cell level that interact and generate the usual homeostatic equilibrium of epithelium as well as other abnormal processes that can disrupt this equilibrium. The structure of this model is based on data from multi-scale quantitative analysis of a unique collection of preneoplastic lesions of the lung, cervix, and oral epithelium, that have been collected in our lab in the last 25 years. In this paper, we are describing the different components of this platform and will present results from different simulations generated by the model.     

Comparing Stochastic Differential Equations and Agent-Based Modelling and Simulation for Early-Stage Cancer

There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary differential equation models, such as the lack of stochasticity, representation of individual behaviours rather than aggregates and individual memory. In this paper we investigate the potential contribution of agent-based modelling and simulation when contrasted with stochastic versions of ODE models using early-stage cancer examples. We seek answers to the following questions: (1) Does this new stochastic formulation produce similar results to the agent-based version? (2) Can these methods be used interchangeably? (3) Do agent-based models outcomes reveal any benefit when compared to the Gillespie results? To answer these research questions we investigate three well-established mathematical models describing interactions between tumour cells and immune elements. These case studies were re-conceptualised under an agent-based perspective and also converted to the Gillespie algorithm formulation. Our interest in this work, therefore, is to establish a methodological discussion regarding the usability of different simulation approaches, rather than provide further biological insights into the investigated case studies. Our results show that it is possible to obtain equivalent models that implement the same mechanisms; however, the incapacity of the Gillespie algorithm to retain individual memory of past events affects the similarity of some results. Furthermore, the emergent behaviour of ABMS produces extra patters of behaviour in the system, which was not obtained by the Gillespie algorithm.     

The epitheliome: agent-based modelling of the social behaviour of cells

We have developed a new computational modelling paradigm for predicting the emergent behaviour resulting from the interaction of cells in epithelial tissue. As proof-of-concept, an agent-based model, in which there is a one-to-one correspondence between biological cells and software agents, has been coupled to a simple physical model. Behaviour of the computational model is compared with the growth characteristics of epithelial cells in monolayer culture, using growth media with low and physiological calcium concentrations. Results show a qualitative fit between the growth characteristics produced by the simulation and the in vitro cell models.     

Hybrid simulation modelling of networks of heterogeneous care homes and the inter-facility spread of Covid-19 by sharing staff

Although system dynamics [SD] and agent-based modelling [ABM] have individually served as effective tools to understand the Covid-19 dynamics, combining these methods in a hybrid simulation model can help address Covid-19 questions and study systems and settings that are difficult to study with a single approach. To examine the spread and outbreak of Covid-19 across multiple care homes via bank/agency staff and evaluate the effectiveness of interventions targeting this group, we develop an integrated hybrid simulation model combining the advantages of SD and ABM. We also demonstrate how we use several approaches adapted from both SD and ABM practices to build confidence in this model in response to the lack of systematic approaches to validate hybrid models. Our modelling results show that the risk of infection for residents in care homes using bank/agency staff was significantly higher than those not using bank/agency staff (Relative risk [RR] 2.65, 95% CI 2.57–2.72). Bank/agency staff working across several care homes had a higher risk of infection compared with permanent staff working in a single care home (RR 1.55, 95%CI 1.52–1.58). The RR of infection for residents is negatively correlated to bank/agency staff’s adherence to weekly PCR testing. Within a network of heterogeneous care homes, using bank/agency staff had the most impact on care homes with lower intra-facility transmission risks, higher staff-to-resident ratio, and smaller size. Forming bubbles of care homes had no or limited impact on the spread of Covid-19. This modelling study has implications for policy makers considering developing effective interventions targeting staff working across care homes during the ongoing and future pandemics.     

Ecosystem engineering as an energy transfer process: a simple agent-based model

Ecosystem engineers are defined as organisms who modulate the availability of resources for themselves and other organisms by physically changing the environment. Ecosystem engineering is a well-recognised ecological interaction, but there is a limited number of general models due to the recent development of the field. Agent-based models are often used to study how organisms respond to changing environments and are suitable for modelling ecosystem engineering. To our knowledge, agent-based methodology has not yet been used to model ecosystem engineering. In this paper, we develop a simple agent-based population dynamics model of ecosystem engineering as an energy transfer process. We apply energy budget approach to conceptually explain how ecosystem engineers transfer energy to the environment and define various types of energy transfers relative to their effects on the engineers and other organisms. We simulate environments with various levels of resource abundance and compare the results of the model without ecosystem engineering agents to the model with ecosystem engineering agents. We find that in environments with higher levels of resources, the presence of ecosystem engineers increases the average carrying capacity and the strength of population fluctuations, while in environments with lower levels of resources, ecosystem engineering mitigates fluctuations, increases average carrying capacity and makes environments more resilient. Finally, we discuss about the further application of agent-based modelling for the theoretical and experimental development of the ecosystem engineering concept.     

Complementary methodology in the analysis of rhythmic data, using examples from a complex situation, the rhythmicity of temperature in night shift workers

The methodology for analyzing a biological rhythm has already been the subject of much investigation. However, many questions still have no answer, for example, questions such as the periods chosen: fixed or determined. Throughout this article, we suggest a somewhat innovative methodology that makes it possible to define the steps that seem essential to us in the scientific analysis of rhythms. For some time, this methodology has been put into practice in our laboratory in various studies, some of which have given rise to publications. The notion of quality is a new notion that is present in industry and, when applied to sampling, can improve experimentation. In this way, one may judge the degree to which data samples can be explored as well as the degree of validity of the results of exploration. We provide several methods for achieving this. The search for periods is also important. For this, we have various methods but we must be able to determine those that are the most appropriate and reliable in a particular case. We propose spectral methods, two of which are new and complement 'Cosinor' methodology. On the other hand, modelling uses various methods such as those from, for example, periodic trigonometric functions or more complex chaos functions. We are interested in models from the field of regression (the cosine model) and complementary statistical tests that make it possible to validate the proposed model.     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统（SES）模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现：（1）多数模型对景观过程与社会经济决策的反馈关系分析不足;（2）应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;（3）通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括：集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

An “Age” Structured Model of Hematopoietic Stem Cell Organization with Application to Chronic Myeloid Leukemia

Previously, we have modeled hematopoietic stem cell organization by a stochastic, single cell-based approach. Applications to different experimental systems demonstrated that this model consistently explains a broad variety of in vivo and in vitro data. A major advantage of the agent-based model (ABM) is the representation of heterogeneity within the hematopoietic stem cell population. However, this advantage comes at the price of time-consuming simulations if the systems become large. One example in this respect is the modeling of disease and treatment dynamics in patients with chronic myeloid leukemia (CML), where the realistic number of individual cells to be considered exceeds 10⁶. To overcome this deficiency, without losing the representation of the inherent heterogeneity of the stem cell population, we here propose to approximate the ABM by a system of partial differential equations (PDEs). The major benefit of such an approach is its independence from the size of the system. Although this mean field approach includes a number of simplifying assumptions compared to the ABM, it retains the key structure of the model including the “age”-structure of stem cells. We show that the PDE model qualitatively and quantitatively reproduces the results of the agent-based approach.     

Formal agent-based modelling of intracellular chemical interactions

Individual-based or agent-based models have proved useful in a variety of different biological contexts. This paper presents an agent-based model using a formal computational modelling approach to model a crucial biological system--the intracellular NF-kappaB signalling pathway. The pathway is vital to immune response regulation, and is fundamental to basic survival in a range of species. Alterations in pathway regulation underlie many diseases, including atherosclerosis and arthritis. Our modelling of individual molecules, receptors and genes provides a more comprehensive outline of regulatory network mechanisms than previously possible with equation-based approaches. The model has been validated with data obtained from single cell experimental analysis.     

Analysing scientific workflows with Computational Tree Logic

Motivated by the widespread use of workflow systems in e-Science applications, this article introduces a formal analysis framework for the verification and profiling of the control flow aspects of scientific workflows. The framework relies on process algebras that characterise each workflow component with a process behaviour, which is then used to build a CTL state model that can be reasoned about. We demonstrate the benefits of the approach by modelling the control flow behaviour of the Discovery Net system, one of the earliest workflow-based e-Science systems, and present how some key properties of workflows and individual service utilisation can be queried at design time. Our approach is generic and can be applied easily to modelling workflows developed in any other system. It also provides a formal basis for the comparison of control aspects of e-Science workflow systems and a design method for future systems.     

Exploring the potential of participatory systems thinking techniques in progressing SLCA

Purpose

There are a range of systems thinking-based methods well established for participatory actions that allow for greater integration of various mental models and understanding of systems that should be considered in advancing engagement methods in SLCA. This paper highlights the potential application of participatory modelling approaches based in systems thinking theory as a potential entry point in stakeholder inclusion and understanding impact pathways and system behaviour in social life cycle assessment (SLCA).

Methods

We discuss the application of various systems thinking methodologies to SLCA, along with pertinent examples from literature, and develop a framework that integrates both methodologies.

Results and discussion

Here we propose three distinct benefits of group modelling approaches; (1) procedural benefits through the ability to be inclusive of mental models, various perspectives and enhance stakeholder conceptualisation of a system; and the ability to combine both (2) qualitative and (3) quantitative analysis techniques under a cohesive framework. We propose the specific merits of combining the use of agent-based (AB) and system dynamic (SD) modelling in SLCA due to the emphasis upon consumer decisions and behaviour and the inherently dynamic non-linear cause-effect chains that are common in social systems.

Conclusions

We conclude that many facets of participatory modelling techniques can align with SLCA across the methodology, particularly if enhanced consideration of stakeholders and their various values is desired. We recommend the further development and inclusion of participatory systems thinking-based frameworks to advance the SLCA methodology with specific reference to the ability to enhance interpretation through the analysis of feedbacks that may not be addressed in current approaches.

     

Novel Multiscale Modeling Tool Applied to Pseudomonas aeruginosa Biofilm Formation

Multiscale modeling is used to represent biological systems with increasing frequency and success. Multiscale models are often hybrids of different modeling frameworks and programming languages. We present the MATLAB-NetLogo extension (MatNet) as a novel tool for multiscale modeling. We demonstrate the utility of the tool with a multiscale model of Pseudomonas aeruginosa biofilm formation that incorporates both an agent-based model (ABM) and constraint-based metabolic modeling. The hybrid model correctly recapitulates oxygen-limited biofilm metabolic activity and predicts increased growth rate via anaerobic respiration with the addition of nitrate to the growth media. In addition, a genome-wide survey of metabolic mutants and biofilm formation exemplifies the powerful analyses that are enabled by this computational modeling tool.     

Agent-based modeling of multicell morphogenic processes during development

A central challenge in the field of developmental biology is to understand how mechanisms at one level of biological scale (i.e., cell-level) interact to produce higher-level (i.e., tissue-level) phenomena. This challenge is particularly relevant to the study of tissue morphogenesis, the process that generates newly formed, remodeled, or regenerated tissue structures. Morphogenesis arises from the spatially- and temporally-dynamic interactions of individual cells with each other and their local environment. Computational models have been combined with experimental efforts to accelerate the discovery processes. Agent-based modeling (ABM) is a computational technique that can be used to model collections of individual biological cells and compute their interactions, which generate emergent tissue-level results. Recently, ABM has been applied to the study of various developmental morphogenic processes, and the purpose of this review is to summarize these studies in order to demonstrate the types of advances that can be expected from pursuing a multicell ABM approach. We also highlight some challenges associated with ABM and suggest strategies for overcoming them. While ABM's application to the study of ecology, epidemiology, and social sciences has a much longer history, we suggest that the application of ABM to the study of morphogenesis has great utility, and when paired with benchtop experimentation, ABM can provide new insights and direct future experimentation.