scholarly journals Data-driven models reveal mutant cell behaviors important for myxobacterial aggregation

2020 ◽  
Author(s):  
Zhaoyang Zhang ◽  
Christopher R. Cotter ◽  
Zhe Lyu ◽  
Lawrence J. Shimkets ◽  
Oleg A. Igoshin
Keyword(s):  
Cell Tracking ◽  
Mutant Cell ◽  
Agent Based Modeling ◽  
Behavioral Changes ◽  
Data Driven ◽  
Cell Behavior ◽  
Fruiting Bodies ◽  
Wild Type ◽  
Agent Based ◽  
Cell Behaviors

AbstractSingle mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using Myxococcus xanthus as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, csgA and pilC, that cannot aggregate unless mixed with wild type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust.

scholarly journals Data-Driven Models Reveal Mutant Cell Behaviors Important for Myxobacterial Aggregation

mSystems ◽  
2020 ◽  
Vol 5 (4) ◽  
Author(s):  
Zhaoyang Zhang ◽  
Christopher R. Cotter ◽  
Zhe Lyu ◽  
Lawrence J. Shimkets ◽  
Oleg A. Igoshin
Keyword(s):  
Cell Tracking ◽  
Mutant Cell ◽  
Myxococcus Xanthus ◽  
Data Driven ◽  
Self Organization ◽  
Cell Behavior ◽  
Significant Heterogeneity ◽  
Wild Type ◽  
Content Type ◽  
Mutant Strains

ABSTRACT Single mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using Myxococcus xanthus as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, csgA and pilC, that cannot aggregate unless mixed with wild-type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT, demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust. IMPORTANCE Self-organization into spatial patterns is evident in many multicellular phenomena. Even for the best-studied systems, our ability to dissect the mechanisms driving coordinated cell movement is limited. While genetic approaches can identify mutations perturbing multicellular patterns, the diverse nature of the signaling cues coupled to significant heterogeneity of individual cell behavior impedes our ability to mechanistically connect genes with phenotype. Small differences in the behaviors of mutant strains could be irrelevant or could sometimes lead to large differences in the emergent patterns. Here, we investigate rescue of multicellular aggregation in two mutant strains of Myxococcus xanthus mixed with wild-type cells. The results demonstrate how careful quantification of cell behavior coupled to data-driven modeling can identify specific motility features responsible for cell aggregation and thereby reveal important synergies and compensatory mechanisms. Notably, mutant cells do not need to precisely recreate wild-type behaviors to achieve complete aggregation.

scholarly journals Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development

2017 ◽  
Vol 114 (23) ◽  
pp. E4592-E4601 ◽  
Author(s):  
Christopher R. Cotter ◽  
Heinz-Bernd Schüttler ◽  
Oleg A. Igoshin ◽  
Lawrence J. Shimkets
Keyword(s):  
Cell Tracking ◽  
Myxococcus Xanthus ◽  
Cell Movement ◽  
Modeling Method ◽  
Data Driven ◽  
Self Organization ◽  
Emergent Properties ◽  
Developmental Cycle ◽  
Fluorescent Cell ◽  
Cell Behaviors

Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell–cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with data-driven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.

Clicking position and user posting behavior in online review systems: A data-driven agent-based modeling approach

2020 ◽  
Vol 512 ◽  
pp. 161-174 ◽  
Author(s):  
Guoyin Jiang ◽  
Xiaodong Feng ◽  
Wenping Liu ◽  
Xingjun Liu
Keyword(s):  
Agent Based Modeling ◽  
Data Driven ◽  
Online Review ◽  
Agent Based ◽  
Modeling Approach

Dynamic-data-driven agent-based modeling for the prediction of evacuation behavior during hurricanes

2021 ◽  
Vol 106 ◽  
pp. 102193
Author(s):  
Seunghan Lee ◽  
Saurabh Jain ◽  
Keeli Ginsbach ◽  
Young-Jun Son
Keyword(s):  
Agent Based Modeling ◽  
Data Driven ◽  
Dynamic Data ◽  
Agent Based ◽  
Evacuation Behavior

scholarly journals Data-driven agent-based modeling, with application to rooftop solar adoption

2016 ◽  
Vol 30 (6) ◽  
pp. 1023-1049 ◽  
Author(s):  
Haifeng Zhang ◽  
Yevgeniy Vorobeychik ◽  
Joshua Letchford ◽  
Kiran Lakkaraju
Keyword(s):  
Agent Based Modeling ◽  
Data Driven ◽  
Agent Based ◽  
Rooftop Solar

scholarly journals Mapping the Landscape of Behavioral Theories: Systematic Literature Review

2019 ◽  
Vol 35 (2) ◽  
pp. 161-179 ◽  
Author(s):  
Heeseo Rain Kwon ◽  
Elisabete A. Silva
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Agent Based Modeling ◽  
Data Driven ◽  
Agent Based ◽  
Search Results ◽  
Behavioral Theories

The term “behavioral” has become a hot topic in recent years in various disciplines; however, there is yet limited understanding of what theories can be considered behavioral theories and what fields of research they can be applied to. Through a cross-disciplinary literature review, this article identifies sixty-two behavioral theories from 963 search results, mapping them in a diagram of four groups (factors, strategies, learning and conditioning, and modeling), and points to five discussion points: understanding of terms, classification, guidance on the use of appropriate theories, inclusion in data-driven research and agent-based modeling, and dialogue between theory-driven and data-driven approaches.

scholarly journals Robust coordination in adversarial social networks: From human behavior to agent-based modeling

2021 ◽  
pp. 1-36
Author(s):  
Chen Hajaj ◽  
Zlatko Joveski ◽  
Sixie Yu ◽  
Yevgeniy Vorobeychik
Keyword(s):  
Human Behavior ◽  
Computational Models ◽  
Human Subjects ◽  
Agent Based Modeling ◽  
Data Driven ◽  
Limited Attention ◽  
Decentralized Coordination ◽  
Agent Based ◽  
Network Topologies ◽  
Do So

Abstract Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.

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