Session M14: Evolutionary and Ecological Dynamics III

Live

Complex systems theory predicts that collective information processing becomes optimal at the border between order and disorder, i.e. at a critical point. Thus, animal collectives have been suggested to be examples of self-organized critical systems. However, evolutionary adaptation towards a possible group-level optimum, implicitly assumes group-level selection in general not relevant for groups of non-related individuals. Furthermore, previous theories rely on abstract models, which ignore spatial self-organization effects. Using a generic, spatially explicit model of collective predator avoidance, we show that schooling prey performance is indeed optimal at criticality, but surprisingly not due to optimal collective response, as predicted by the criticality hypothesis, but due to dynamical group structure. More importantly, structural sensitivity makes the critical state evolutionary highly unstable, demonstrating the crucial importance of spatial self-organization when discussing evolutionary benefits of collective animal behavior.   

Live

All living cells synthesize proteins by transcribing the hereditary information in their DNA into strands of messenger RNA which are subsequently translated into amino acid sequences. The genetic code that assigns triplets of nucleotides (codons) to their corresponding amino acids is redundant, since most amino acids are encoded by several codons. Mutations that change the DNA sequence (and hence the sequence of codons) but leave the amino acid sequence unchanged are called synonymous. Motivated by recent experiments on an antibiotic resistance gene, in this work we investigate genetic interactions between synonymous mutations in the framework of exclusion models of translation. We show that the range of possible interactions is markedly different depending on whether translation efficiency is assumed to be proportional to ribosome current or ribosome speed. In the first case every mutational effect has a definite sign that is independent of genetic background, whereas in the second case the effect-sign can vary depending on the presence of other mutations. The latter result is demonstrated using configurations of multiple translational bottlenecks induced by slow codons.   

Live

We construct a pathwise formulation of a growing population of cells, based on two samplings of the lineages within the population, namely the forward and backward samplings. We show that a general symmetry relation, called fluctuation relation and similar to Crooks fluctuation theorem in Stochastic Thermodynamics,
relates these two samplings independently of the model used to generate divisions and growth in the population. This symmetry implies general
inequalities between the mean number of divisions and the growth rate of the population, and in the long time limit, between the mean generation time and the population doubling time [1,2].
This relation also leads to various universal inequalities constraining the fluctuations in lineage trees of a phenotypic trait of interest or of its fitness, which quantifies the correlations between the trait and the number of divisions.
We illustrate our results with numerical simulations of growing cell populations and with data from time-lapse video-microscopy experiments of bacteria colonies.

[1] Garcia-Garcia et al., Phys. Rev. E, 042413 (2019)
[2] A. Genthon et al., Sci. Rep. 10, 11889 (2020)   

Live

Deep mutational scanning (DMS) is a popular method used to test the functional effects of mutations at very large scales. DMS experiments generate large amounts of data that can be difficult to analyze, with large variation between experimental replicates. Here, we combined methods from statistical physics and population genetics to interpret the functional effects of mutations from DMS data. We applied our approach to DMS data from both human proteins and viruses. Through extensive tests, we find that our method infers substantially more consistent functional effects of mutations than current state-of-the-art methods, while also being computationally efficient. Our pipeline can be widely applied to DMS data including multiple time points, replicates, and conditions.   

Live

By the end of May 2020, all states in the US have eased their COVID-19 mitigation measures. Different states adopted markedly different policies and timing in reopening. An important question is how the relaxation of mitigation measures is related to the number of casualties. To address this question, we compare the hypothetical case in which the mitigation measures are left intact to the actual data. We find that different states have shown significant differences in the number of deaths, possibly due to their different policies and reopening schedules. Our study provides a gauge for different state approaches and can serve as a guide for implementing best policies in the future. Our data also points to the fact that the face mask mandate has the strongest correlation with the death count of all of the mitigation policies.   

Live

Cooperative interactions within the prey swarm play a crucial role in determining the escape dynamics in the presence of a predator. Using a simple particle-based model, incorporating the essential attractive and repulsive interactions between the prey and the predator, we investigate the effect of cooperative interactions on the survival chances of a prey swarm attacked by a nearby predator. The range of interaction within the prey group varies due to their vision, age, and even physical structure. Our study has inferred that the prey group cannot escape the predator if the interaction range is very small or very large. However, the survival probability of the prey swarm turns out to be maximum in the intermediate regime of interaction range. Moreover, different escape patterns emerge as the range of interaction changes. Further, our study shows that the size of the prey group and predator strength have an immense effect on this optimal survival regime.

Reference: Chakraborty, D., Bhunia, S., & De, R. (2020). Survival chances of a prey swarm: how the cooperative interaction range affects the outcome. Scientific Reports, 10(1), 1-8.   

Live

Evolutionary Game Theory studies how strategies evolve in populations and predicts the evolutionarily stable state. The theory can be extended to describe stochastic strategies and predicts that the stable stochastic strategy is determined by probabilities that reflect the corresponding mixture of deterministic strategies. However, much less is known about populations of stochastic strategies that evolve at a finite mutation rate, where multiple mutants can segregate in the population at the same time. Here we show that in this limit, the stable state is a distribution of strategies that is the stationary solution of a Fokker-Planck equation. This distribution is equivalent to the quasi-species distribution in population genetics, which we call the "quasi-strategy". We explicitly solve the Fokker-Planck equation for stochastic games with two and three moves, and then show numerically that for iterated stochastic memory-one games at finite mutation rate, the quasi-strategy's mutational robustness allows it to outcompete the "optimal'' generous ZD strategies that cannot be invaded in the small mutation limit. This work suggests that the concept of quasi-strategies may have wide-ranging applications in biological settings in which phenotypic plasticity is observed.   

Live

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. The number of possible communities grows exponentially with the number of species, so an exhaustive search cannot be performed even for a dozen species. We investigate how the success of a heuristic search for the optimal combination of microbes depends on community ecology. Using consumer-resource models with and without cross-feeding, we show that search success depends on the ruggedness of the appropriately-defined ecological landscape. We identify ruggedness measures robust to noise and low sampling density that can be used to predict the performance of a heuristic search from realistic experimental data. More importantly, we report how the patterns of species interactions influence landscape ruggedness. This relationship can guide the choice of the species and their environment in biotechnology applications and allows one to assess the likelihood of finding a high-performance microbial community. We show the feasibility of our approach using experimental data from simple soil microbial communities.   

Live

A sudden growth of photosynthetic algal cells causes Harmful Algal Bloom (HAB), depleting water resources and disrupting the balance of aqua ecosystems. HAB is an emerging environmental problem acerbated by climate change and population growth. Despite the urgency of the problem, there still lacks a systematic understanding of the environmental conditions (physical, chemical, and biological) under which HABs occur. In this presentation, we studied the growth of a model algal strain, Chlamydomonas reinhardtii, under a dual concentration gradient of nitrogen (N) and phosphorous (P) and found that N and P synergistically promoted algal cell growth. Interestingly, no discernible response was observed under single nutrient gradient. We also demonstrated the potential application of the newly developed microfluidic platform that integrated the array microhabitat format with dual gradient generation, enabling fast screening of environmental factors for algal growth studies. Future investigations will include the dynamics of bacterial community under controlled environmental condition.   

Live

Nutrient cycling plays a critical role in determining ecosystem structure at all scales, from microbial communities to the entire biosphere. Therefore, a central problem in ecology is understanding how ecosystems are organized to robustly cycle nutrients. Here we use closed microbial ecosystems (CES), hermetically sealed microbial consortia that sustain nutrient cycles when provided with only light, to address this problem in the context of carbon cycling. We develop a new technique for quantifying carbon cycling in hermetically sealed microbial communities and show that CES comprised of an alga and diverse bacterial consortia self-organize to cycle carbon. Self-organized CES sustain carbon cycles for months. Comparing a library of CES, we find that carbon cycling does not depend strongly on the taxonomy of the bacteria present. Measurements of the carbon utilization capabilities in CES reveals functional redundancy: despite strong taxonomic differences, self-organized CES exhibit a conserved set of metabolic capabilities. Therefore, an emergent carbon cycle enforces metabolic, but not taxonomic constraints on ecosystem organization.   

Live

The stability and dynamics of complex ecosystems is a longstanding puzzle in ecology. Statistical physics approaches have shown that when the number of interacting species is high, surprisingly general statements can be made about the species abundances. While most of these approaches assume well-mixed ecosystems where diversity is maintained by continuous speciation, natural ecosystems are often composed of many local communities between which individuals migrate. The self-organization in these metacommunities is, however, poorly understood. Here we study metacommunities with migration on two length scales: nearest-neighbor and global coupling. Taking into account competitive interactions and demographic noise, we find that when the number of species is large the species' dynamics operates at a critical point which is dominated by demographic fluctuations. In spatially extended systems this results in fractal abundance patterns. For global coupling we derive a mean field theory that yields analytic expressions for the species abundances. In summary, our study reveals a general self-organization process of species-rich metacommunities with important consequences for pattern formation and diversity in spatially extended ecosystems.   

Live

Neutral theories of biodiversity assume that all individuals are functionally identical regardless of the species, whereas symmetrical non-neutral (i.e “niche”) theories forego the assumption of equivalent individuals and distinguish solely between self and non-self interactions. Although species survival in both theories depends on stochasticity, competitive niche overlap in non-neutral theories also affects the species abundance distribution; coexistence and dominance regimes have been observed in both theories in previous works. Using a minimal model of interacting species, we have comprehensively investigated how the species abundance distribution changes between different regimes defined by immigration rate and the niche overlap transitions. We also identify a previously unknown regime where the abundance distribution is multimodal. We show that the transitions between different coexistence regimes is controlled by the balance between the immigration rate and the extinction times of individual species, and show how the species abundance distribution correlates with the species richness. Our results provide a framework for interpreting the discrepancies of abundances in ecological data and inferring the underlying dynamics that shape communities of interacting species.   

Live

Genetically identical bacterial cells, even in identical environments, can display significant variability in their phenotypic behaviors such as their growth rates and division times. The statistics of these phenotypic behaviors are set by the environment as well as the genotype. The fate of a new mutation in a homogenous population is significantly affected by the short transient dynamics at low copy numbers which is heavy influenced by the phenotypic variability. In this talk, we discuss what aspects of variability in growth and divisions of single cells affect population genetics and are in turn under selection pressure.   

Live

Multicellular organisms are pervasive and broadly successful. Their success is due in part to their complex structures and functions, which self-assemble through a genetically regulated multicellular development plan. However, early multicellular groups likely lacked the developmental genes necessary to ensure that group traits were heritable. Without developmental genes, cells divide stochastically, meaning that the final cellular configuration emerges at the whim of random events, making it unclear how nascent multicellular groups achieve heritable size and shape. Here, we propose that stochasticity supplied by random cell division can directly lead to highly heritably group traits. We find that experimentally evolved multicellular groups formed with fixed bonds via stochastic cell division achieve robust, heritable multicellular size and reproduction due to emergent statistics from entropic cellular packing. Our results indicate that emergent physics plays a critical role in establishing cellular groups as viable ``Darwinian materials’’, and that both stochastic growth and fixed bonds may have been crucial ingredients for many currently surviving branches of complex multicellularity.   

On Demand

Planktonic communities in the oceans host a huge number of species. In this talk, I will introduce a spatial coalescence model that predict the impact of oceanic turbulence on the diversity of these communities. The model predicts that oceanic currents cause a steeper decay the species abundance distribution, thus shaping more diverse communities. We confirm our predictions by comparing metabarcoding data from oceans and freshwater ecosystems.

Reference: P. Villa-Martin, A. Bucek, T. Bourguignon, S. Pigolotti, Science Advances 6 (29), eaaz9037 (2020).