scholarly journals Shared behavioral mechanisms underlie C. elegans aggregation and swarming

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Siyu Serena Ding ◽  
Linus J Schumacher ◽  
Avelino E Javer ◽  
Robert G Endres ◽  
André EX Brown

In complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming—a dynamic phenotype only observed at longer timescales. Using fluorescence multi-worm tracking, we quantify aggregation in terms of individual dynamics and population-level statistics. Then we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules for aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation.

2018 ◽  
Author(s):  
S Serena Ding ◽  
Linus J. Schumacher ◽  
Avelino E. Javer ◽  
Robert G. Endres ◽  
André EX Brown

AbstractIn complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While such collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming—a dynamic phenotype only observed at longer timescales. Using fluorescent multi-worm tracking, we quantify aggregation behavior in terms of individual dynamics and population-level statistics. Based on our quantification, we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules that give rise to aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation. Hence, mesoscopic C. elegans uses mechanisms familiar from microscopic systems for aggregation, but implemented via more complex behaviors characteristic of macroscopic organisms.


Author(s):  
Iris Lorscheid ◽  
Matthias Meyer

AbstractDespite advances in the field, we still know little about the socio-cognitive processes of team decisions, particularly their emergence from an individual level and transition to a team level. This study investigates team decision processes by using an agent-based model to conceptualize team decisions as an emergent property. It uses a mixed-method research design with a laboratory experiment providing qualitative and quantitative input for the model’s construction, as well as data for an output validation of the model. First, the laboratory experiment generates data about individual and team cognition structures. Then, the agent-based model is used as a computational testbed to contrast several processes of team decision making, representing potential, simplified mechanisms of how a team decision emerges. The increasing overall fit of the simulation and empirical results indicates that the modeled decision processes can at least partly explain the observed team decisions. Overall, we contribute to the current literature by presenting an innovative mixed-method approach that opens and exposes the black box of team decision processes beyond well-known static attributes.


2021 ◽  
Author(s):  
Santiago Romero-Brufau ◽  
Ayush Chopra ◽  
Alex J Ryu ◽  
Esma Gel ◽  
Ramesh Raskar ◽  
...  

AbstractObjectivesTo estimate population health outcomes under delayedsecond dose versus standard schedule SARS-CoV-2 mRNA vaccination.DesignAgent-based modeling on a simulated population of 100,000 based on a real-world US county. The simulation runs were replicated 10 times. To test the robustness of these findings, simulations were performed under different estimates for single-dose efficacy and vaccine administration rates, and under the possibility that a vaccine prevents only symptoms but not asymptomatic spread.Settingpopulation level simulation.Participants100,000 agents are included in the simulation, with a representative distribution of demographics and occupations. Networks of contacts are established to simulate potentially infectious interactions though occupation, household, and random interactionsInterventionswe simulate standard Covid-19 vaccination, versus delayed-second-dose vaccination prioritizing first dose. Sensitivity analyses include first-dose vaccine efficacy of 70%, 80% and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread; and an alternative vaccination strategy that implements delayed-second-dose only for those under 65 years of age.Main outcome measurescumulative Covid-19 mortality over 180 days, cumulative infections and hospitalizations.ResultsOver all simulation replications, the median cumulative mortality per 100,000 for standard versus delayed second dose was 226 vs 179; 233 vs 207; and 235 vs 236; for 90%, 80% and 70% first-dose efficacy, respectively. The delayed-second-dose strategy was optimal for vaccine efficacies at or above 80%, and vaccination rates at or below 0.3% population per day, both under sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100,000. The delayed-second-dose for those under 65 performed consistently well under all vaccination rates tested.ConclusionsA delayed-second-dose vaccination strategy, at least for those under 65, could result in reduced cumulative mortality under certain conditions.


Author(s):  
Mohan Matthen

Physicalism appears to undermine the autonomy of ‘special sciences’ such as biology, and to leave little room for proprietary biological laws or causation. Mendel’s ‘Laws’ are so-called because they are fundamental to the subject-area, but since they describe causal processes that are wholly physical in nature, they seem to reduce to physical laws, given certain propositions about the composition of DNA. The same goes for other principles of the biological sciences. This argument has been challenged by Hilary Putnam, on the grounds that good explanations, for instance in mathematical terms, could range more widely than any given physical realization. Putnam argues that mathematics could thus have an autonomous role in science despite physicalism. Putnam’s insight has been applied to classical genetics by Philip Kitcher. A gene is a unit of inheritance that passes unchanged from parent to offspring according to certain rules. It is these rules that are essential to understanding inheritance, not details of interaction in the DNA substrate. Putnam and Kitcher here employ a notion similar to Aristotle’s ‘formal causes’ – functional and structural determinants of biological characteristics that are somewhat independent of material constitution. There are other conceptions of laws to be found in philosophy of science. Some think that they are propositions with the capacity to impart axiomatic structure to what is known about a domain. The principle of natural selection plays this role in biology, though it is a priori. Again, some think that laws are necessary truths: on cladistic systems of classification, the proposition that the common raven is a bird is arguably a law under this understanding. The nature of causal patterns in natural selection has been a matter of some discussion recently. The view that individual-level causes are sufficient to explain selection-outcomes is tempting to the reductionist, but distorts the explanatory aims of evolutionary theory. Clearly, evolutionary theory requires population-level causes. On the other hand, it has been questioned whether natural selection itself should be understood as a ‘force’ acting on a population, somewhat in the same manner as gravitation acts on a body. Statistical views of natural selection seek alternatives to this way of understanding selection. Finally, what are biological entities? Some ontologies admit no priority among collections of atoms – the argument is that an organism, for instance, is nothing more than such a collection. Many biologists, however, treat of composite entities as internally organized complex systems. On this view, cells, organisms, populations, and ecosystems have privileged ontological status.


2012 ◽  
Vol 54 (1-2) ◽  
pp. 37-49 ◽  
Author(s):  
BENJAMIN J. BINDER ◽  
JOSHUA V. ROSS ◽  
MATTHEW J. SIMPSON

AbstractWe consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.


2021 ◽  
Author(s):  
Mason Youngblood ◽  
David Lahti

In this study, we used a longitudinal dataset of house finch (Haemorhous mexicanus) song recordings spanning four decades in the introduced eastern range to assess how individual-level cultural transmission mechanisms drive population-level changes in birdsong. First, we developed an agent-based model (available as a new R package called TransmissionBias) that simulates the cultural transmission of house finch song given different parameters related to transmission biases, or biases in social learning that modify the probability of adoption of particular cultural variants. Next, we used approximate Bayesian computation and machine learning to estimate what parameter values likely generated the temporal changes in diversity in our observed data. We found evidence that strong content bias, likely targeted towards syllable complexity, plays a central role in the cultural evolution of house finch song in western Long Island. Frequency and demonstrator biases appear to be neutral or absent. Additionally, we estimated that house finch song is transmitted with extremely high fidelity. Future studies should use our simulation framework to better understand how cultural transmission and population declines influence song diversity in wild populations.


2021 ◽  
Author(s):  
A.R. Lynch ◽  
N.L. Arp ◽  
A.S. Zhou ◽  
B.A. Weaver ◽  
M.E. Burkard

ABSTRACTChromosomal instability (CIN) — persistent chromosome gain or loss through abnormal karyokinesis — is a hallmark of cancer that drives aneuploidy. Intrinsic chromosome mis-segregation rates, a measure of CIN, can inform prognosis and are a likely biomarker for response to anti-microtubule agents. However, existing methodologies to measure this rate are labor intensive, indirect, and confounded by karyotype selection reducing observable diversity. We developed a framework to simulate and measure CIN, accounting for karyotype selection, and recapitulated karyotype-level clonality in simulated populations. We leveraged approximate Bayesian computation using phylogenetic topology and diversity to infer mis-segregation rates and karyotype selection from single-cell DNA sequencing data. Experimental validation of this approach revealed extensive chromosome mis-segregation rates caused by the chemotherapy paclitaxel (17.5±0.14/division). Extending this approach to clinical samples revealed the inferred rates fell within direct observations of cancer cell lines. This work provides the necessary framework to quantify CIN in human tumors and develop it as a predictive biomarker.


2020 ◽  
Vol 47 (2) ◽  
pp. 224-234
Author(s):  
Charlotte Probst ◽  
Tuong Manh Vu ◽  
Joshua M. Epstein ◽  
Alexandra E. Nielsen ◽  
Charlotte Buckley ◽  
...  

Background. By defining what is “normal,” appropriate, expected, and unacceptable, social norms shape human behavior. However, the individual-level mechanisms through which social norms impact population-level trends in health-relevant behaviors are not well understood. Aims. To test the ability of social norms mechanisms to predict changes in population-level drinking patterns. Method. An individual-level model was developed to simulate dynamic normative mechanisms and behavioral rules underlying drinking behavior over time. The model encompassed descriptive and injunctive drinking norms and their impact on frequency and quantity of alcohol use. A microsynthesis initialized in 1979 was used as a demographically representative synthetic U.S. population. Three experiments were performed in order to test the modelled normative mechanisms. Results. Overall, the experiments showed limited influence of normative interventions on population-level alcohol use. An increase in the desire to drink led to the most meaningful changes in the population’s drinking behavior. The findings of the experiments underline the importance of autonomy, that is, the degree to which an individual is susceptible to normative influence. Conclusion. The model was able to predict theoretically plausible changes in drinking patterns at the population level through the impact of social mechanisms. Future applications of the model could be used to plan norms interventions pertaining to alcohol use as well as other health behaviors.


PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0166551 ◽  
Author(s):  
Zi Wang ◽  
Benjamin J. Ramsey ◽  
Dali Wang ◽  
Kwai Wong ◽  
Husheng Li ◽  
...  

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