Agent-based computational models to explore diffusion of medical innovations among cardiologists

2018 ◽  
Vol 112 ◽  
pp. 158-165 ◽  
Author(s):  
Raul A. Borracci ◽  
Mariano A. Giorgi
2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.


Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 90
Author(s):  
Nicolò Cogno ◽  
Roman Bauer ◽  
Marco Durante

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).


2020 ◽  
pp. 369-389
Author(s):  
Sara Montagna ◽  
Andrea Omicini

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored, focussing on the challenging scenarios of developmental biology. After motivating why agent-based abstractions are critical in representing multicellular systems behaviour, MABS is discussed as the source of the most natural and appropriate mechanism for analysing the self-organising behaviour of systems of cells. As a case study, an application of MABS to the development of Drosophila Melanogaster is finally presented, which exploits the ALCHEMIST platform for agent-based simulation.


2011 ◽  
Vol 3 (1) ◽  
pp. 11-26 ◽  
Author(s):  
Radu Burete ◽  
Amelia Badica ◽  
Costin Badica ◽  
Florin Moraru

Trust is a very important quality attribute of an e-service. In particular, the increasing complexity of the e-business environment requires the development of new computational models of trust and reputation for e-business agents. In this paper, the authors introduce a new reputation model for agents engaged in e-business transactions. The model enhances classic reputation models by the addition of forgiveness factor and the use of new sources of reputation information based on agents groups. The paper proposes an improvement of this model by employing the recent con-resistance concept. Finally, the authors show how the model can be used in an agent-based market environment where trusted buyer and seller agents meet, negotiate, and transact multi-issue e-business contracts. The system was implemented using JADE multi-agent platform and initially evaluated on a sample set of scenarios. The paper introduces the design and implementation of the agent-based system together with the experimental scenarios and results.


2019 ◽  
Vol 31 (1) ◽  
pp. 115-148
Author(s):  
Frieder Lempp

Purpose The purpose of this paper is to introduce a new agent-based simulation model of bilateral negotiation based on a synthesis of established theories and empirical studies of negotiation research. The central units of the model are negotiators who pursue goals, have attributes (trust, assertiveness, cooperativeness, creativity, time, etc.) and perform actions (proposing and accepting offers, exchanging information, creating value, etc). Design/methodology/approach Methodologically, the model follows the agent-based approach to modeling. This approach is chosen because negotiations can be described as complex, non-linear systems involving autonomous agents (i.e. the negotiators), who interact with each other, pursue goals and perform actions aimed at achieving their goals. Findings This paper illustrates how the model can simulate experiments involving variables such as negotiation strategy, creativity, reservation value or time in negotiation. An example simulation is presented which investigates the main and interaction effects of negotiators’ reservation value and their time available for a negotiation. A software implementation of the model is freely accessible at https://tinyurl.com/y7oj6jo8. Research limitations/implications The model, as developed at this point, provides the basis for future research projects. One project could address the representation of emotions and their impact on the process and outcome of negotiations. Another project could extend the model by allowing negotiators to convey false information (i.e. to bluff). Yet another project could be aimed at refining the routines used for making and accepting offers with a view to allow parties to reach partial settlements during a negotiation. Practical implications Due to its broad scope and wide applicability, the model can be used by practitioners and researchers alike. As a decision-support system, the model allows users to simulate negotiation situations and estimate the likelihood of negotiation outcomes. As a research platform, it can generate simulation data in a cost- and time-effective way, allowing researchers to simulate complex, large-N studies at no cost or time. Originality/value The model presented in this paper synthesizes in a novel way a comprehensive range of concepts and theories of current negotiation research. It complements other computational models, in that it can simulate a more diverse range of negotiation strategies (distributive, integrative and compromise) and is applicable to a greater variety of negotiation scenarios.


Challenges ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 13
Author(s):  
Bernard Amadei

This paper explores the applicability of the agent-based (AB) and system dynamics (SD) methods to model a case study of the management of water field services. Water borehole sites are distributed over an area and serve the water needs of a population. The equipment at all borehole sites is managed by a single water utility that has adopted specific repair, replacement, and maintenance rules and policies. The water utility employs several service crews initially stationed at a single central location. The crews respond to specific operation and maintenance requests. Two software modeling tools (AnyLogic and STELLA) are used to explore the benefits and limitations of the AB and SD methods to simulate the dynamic being considered. The strength of the AB method resides in its ability to capture in a disaggregated way the mobility of the individual service crews and the performance of the equipment (working, repaired, replaced, or maintained) at each borehole site. The SD method cannot capture the service crew dynamics explicitly and can only model the average state of the equipment at the borehole sites. Their differences aside, both methods offer policymakers the opportunity to make strategic, tactical, and logistical decisions supported by integrated computational models.


2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Francis H. Harlow

This paper describes extensions of computational fluid dynamics (CFD) to fields of analysis lying well beyond their current realms of application. In particular, three examples are presented. The first is to the collective behavior of mobs of people interacting with sources of danger and/or opportunity to which each individual responds by actions that depend strongly on the inducement of fear and/or excitement, depending on the intrinsic susceptibilities of the person. This behavior results in both individual activities (agent-based) and collective behaviors (crowd-based stochastic) with consequences of potentially great significance. Extensions are also described for which various other emotional developments are important to the behavior of a mob. The second example is to the processes of biological evolution, in particular to the driving forces that influence the directions of species alterations through a succession of characteristics that are tested for survivability in classical Darwinian fashion. The key to the analysis lies in the newly emerging field of epigenetics, in which numerous important experimental studies are producing astonishing results leading to major challenges to the creation of computational models of the collective fluid-like dynamics of interacting biological species. The third example explores an alternative to the Big Bang theory for describing the origin of our universe. The idea is that a parent universe exists, being composed of energy, matter, and antimatter in various forms. In some region a perturbation occurs, which locally has an excess of matter over antimatter. An enormous gravitational buildup of matter and energy in the region leads to a black hole, in which there is distortion in the fourth dimension. The result then leads to an offspring entity (universe) that becomes completely detached from the parent. To apply computational fluid dynamics to the analysis of this process requires formulations that include a major component of relevant physical representations. In all three of these examples, instabilities, fluctuations, and turbulence play major roles. These arise naturally in agent-based numerical formulations (the first and second of our examples), but are much more challenging to describe in a stochastic representation (e.g., the Navier–Stokes equations). Some promising spectral analysis extensions for stochastic formulations are included in this paper.


2015 ◽  
Vol 2 (6) ◽  
pp. 140533 ◽  
Author(s):  
Naoki Masuda ◽  
Thomas A. O'shea-Wheller ◽  
Carolina Doran ◽  
Nigel R. Franks

Collective decision-making is a characteristic of societies ranging from ants to humans. The ant Temnothorax albipennis is known to use quorum sensing to collectively decide on a new home; emigration to a new nest site occurs when the number of ants favouring the new site becomes quorate. There are several possible mechanisms by which ant colonies can select the best nest site among alternatives based on a quorum mechanism. In this study, we use computational models to examine the implications of heterogeneous acceptance thresholds across individual ants in collective nest choice behaviour. We take a minimalist approach to develop a differential equation model and a corresponding non-spatial agent-based model. We show, consistent with existing empirical evidence, that heterogeneity in acceptance thresholds is a viable mechanism for efficient nest choice behaviour. In particular, we show that the proposed models show speed–accuracy trade-offs and speed–cohesion trade-offs when we vary the number of scouts or the quorum threshold.


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