Rules of Engagement: A Guide to Developing Agent-Based Models.

Marc Griesemer, Suzanne S Sindi

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Ecological modelling approaches for predicting emergent properties in microbial communities.Nature ecology & evolution · 2022
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Marc GriesemerControls and Data Systems Division, SLAC National Accelerator Laboratory, Menlo Park, CA, USA.

Suzanne S SindiDepartment of Applied Mathematics, University of California, Merced, Merced, CA, USA. ssindi@ucmerced.edu.

Funding

Mathematical Strategies to Uncover the Molecular Basis of Prion Transitions R01GM126548 · NIGMS · UNIVERSITY OF CALIFORNIA, MERCED · PI SINDI, SUZANNE · 2017 to 2020

$854k

NIGMS NIH HHS R01 GM126548

6 · The paper itself

Abstract

Agent-based models (ABM), also called individual-based models, first appeared several decades ago with the promise of nearly real-time simulations of active, autonomous individuals such as animals or objects. The goal of ABMs is to represent a population of individuals (agents) interacting with one another and their environment. Because of their flexible framework, ABMs have been widely applied to study systems in engineering, economics, ecology, and biology. This chapter is intended to guide the users in the development of an agent-based model by discussing conceptual issues, implementation, and pitfalls of ABMs from first principles. As a case study, we consider an ABM of the multi-scale dynamics of cellular interactions in a microbial community. We develop a lattice-free agent-based model of individual cells whose actions of growth, movement, and division are influenced by both their individual processes (cell cycle) and their contact with other cells (adhesion and contact inhibition).

Indexed as

Models, BiologicalAnimalsHumansAgent-based modelsIndividual-based modelsLattice-freeStochastic simulations

Identifiers

PMID34719003

DOI10.1007/978-1-0716-1585-0_16

PMCPMC13285862

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.