pwc-archive/papers-with-abstracts huggingface dataset:
Metabolic Allometric Scaling of Unicellular Organisms as a Product of Selection Guided by Optimization of Nutrients Distribution in Food Chains
Authors: Yuri K. Shestopaloff
Date: 2016-12-14 00:00:00

One of the major characteristics of living organisms is metabolic rate, which is the amount of energy produced per unit of time. When the mass of organisms increases, the metabolic rate also increases (usually as a power function of mass), but usually slower than mass. This effect is called metabolic allometric scaling. Its causes are considered unknown. The effect has important implications for individual and population organismal development. It was shown in the first part of this study, presented in a separate paper, that in the case of multicellular organisms, this effect is a consequence of natural selection and optimization of nutrient distribution between the species of a food chain, sharing resources of a common habitat. Here, in the second part that studies unicellular organisms, we discover that the same principle of natural selection guided by optimization of nutrient distribution between the species of a food chain defines also metabolic allometric scaling of unicellular organisms. To find that, we consider the metabolic properties of Amoeba proteus, fission yeast Schizosaccharomyces pombe, Escherichia coli, Bacillus subtilis, Staphylococcus. The sharing of nutrients is optimized in such a way that bigger microorganisms have progressively bigger nutrient influx per unit of surface. This evolutionary arrangement secures the stability of a food chain by providing certain metabolic advantages for bigger organisms. Accounting for this regular increase of nutrient influx with mass increase, we obtained allometric exponents and their ranges close to experimental values, thus proving that metabolic allometric scaling of both multicellular and unicellular organisms is defined by the same fundamental evolutionary principle of optimized sharing of nutrients between the species of a food chain.
Metabolic allometric scaling of multicellular organisms as a product of evolutionary development and optimization of food chains
Authors: Yuri K. Shestopaloff
Date: 2016-12-01 00:00:00

Production of energy is a foundation of life. Metabolic rate of organisms (amount of energy produced per unit time) generally increases slower than organisms' mass, which has important implications for life organization. This phenomenon, when considered across different taxa, is called interspecific allometric scaling. Its origin has puzzled scientists for many decades, and still is considered unknown. In this paper we posit that natural selection, as determined by evolutionary pressures, leads to distribution of resources, and accordingly energy, within a food chain, which is optimal from the perspective of stability of the food chain, when each species has sufficient amount of resources for continuous reproduction, but not too much to jeopardize existence of other species. Metabolic allometric scaling is then a quantitative representation of this optimal distribution. Taking locomotion and the primary mechanism for distribution of energy, we developed a biomechanical model to find energy expenditures, considering limb length, skeleton mass and speed. Using the interspecific allometric exponents for these three measures and substituting into the locomotion-derived model for energy expenditure, we calculated allometric exponents for mammals, reptiles, fish and birds, and compared these values with allometric exponents derived from experimental observations. The calculated allometric exponents were nearly identical to experimentally observed exponents for mammals, and very close for fish, reptiles and the basal metabolic rate of birds. The main result of the study is that the metabolic allometric scaling is a function of a mechanism of optimal energy distribution between the species of a food chain. This optimized sharing of common resources provides stability of a food chain for a given habitat and is guided by evolutionary pressures and natural selection.
On the Mathematics of Fraternal Birth Order Effect and the Genetics of Homosexuality
Authors: Tanya Khovanova
Date: 2017-11-01 00:00:00

Mathematicians have always been attracted to the field of genetics. I am especially interested in the mathematical aspects of research on homosexuality. Certain studies show that male homosexuality may have a genetic component that is correlated with female fertility. Other studies show the existence of the fraternal birth order effect, that is the correlation of homosexuality with the number of older brothers. This paper is devoted to the mathematical aspects of how these two phenomena are interconnected. In particular, I show that the fraternal birth order effect produces a correlation between homosexuality and maternal fecundity. Vice versa, I show that the correlation between homosexuality and female fecundity implies the increase of the probability of the younger brothers being homosexual.
Demographic noise can promote abrupt transitions in ecological systems
Authors: Sabiha Majumder, Ayan Das, Appilineni Kushal, Sumithra Sankaran, Vishwesha Guttal
Date: 2021-03-29 00:00:00

Strong positive feedback is considered a necessary condition to observe abrupt shifts of ecosystems. A few previous studies have shown that demographic noise -- arising from the probabilistic and discrete nature of birth and death processes in finite systems -- makes the transitions gradual or continuous. In this paper, we show that demographic noise may, in fact, promote abrupt transitions in systems that would otherwise show continuous transitions. We present our methods and results in a tutorial-like format. We begin with a simple spatially-explicit individual-based model with local births and deaths influenced by positive feedback processes. We then derive a stochastic differential equation that describes how local probabilistic rules scale to stochastic population dynamics. The infinite-size well-mixed limit of this SDE for our model is consistent with mean-field models of abrupt regime-shifts. Finally, we analytically show that as a consequence of demographic noise, finite-size systems can undergo abrupt shifts even with weak positive interactions. Numerical simulations of our spatially-explicit model confirm this prediction. Thus, we predict that small-sized populations and ecosystems may undergo abrupt collapse even when larger systems - with the same microscopic interactions - show a smooth response to environmental stress.
Deep generative selection models of T and B cell receptor repertoires with soNNia
Authors: Giulio Isacchini, Aleksandra M Walczak, Thierry Mora, Armita Nourmohammad
Date: 2020-11-05 00:00:00
Tasks: BIG-bench Machine Learning, Diversity

Subclasses of lymphocytes carry different functional roles to work together to produce an immune response and lasting immunity. Additionally to these functional roles, T and B-cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically using only repertoire level sequence information, we classify CD4$^+$ and CD8$^+$ T-cells, find correlations between receptor chains arising during selection and identify T-cells subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.
A compendium of covariances and correlation coefficients of coalescent tree properties
Authors: Egor Alimpiev, Noah A Rosenberg
Date: 2021-03-24 00:00:00

Gene genealogies are frequently studied by measuring properties such as their height ($H$), length ($L$), sum of external branches ($E$), sum of internal branches ($I$), and mean of their two basal branches ($B$), and the coalescence times that contribute to the other genealogical features ($T$). These tree properties and their relationships can provide insight into the effects of population-genetic processes on genealogies and genetic sequences. Here, under the coalescent model, we study the 15 correlations among pairs of features of genealogical trees: $H_n$, $L_n$, $E_n$, $I_n$, $B_n$, and $T_k$ for a sample of size $n$, with $2 \leq k \leq n$. We report high correlations among $H_n$, $L_n$, $I_n,$ and $B_n$, with all pairwise correlations of these quantities having values greater than or equal to $\sqrt{6} [6 \zeta(3) + 6 - \pi^2] / ( \pi \sqrt{18 + 9\pi^2 - \pi^4}) \approx 0.84930$ in the limit as $n \rightarrow \infty$. Although $E_n$ has an expectation of 2 for all $n$ and $H_n$ has expectation 2 in the limit as $n \rightarrow \infty$, their limiting correlation is 0. The results contribute toward understanding features of the shapes of coalescent trees.
Epidemics from the Eye of the Pathogen
Authors: Faryad Darabi Sahneh, William Fries, Joseph C. Watkins, Joceline Lega
Date: 2021-03-23 00:00:00

While a common trend in disease modeling is to develop models of increasing complexity, it was recently pointed out that outbreaks appear remarkably simple when viewed in the incidence vs. cumulative cases (ICC) plane. This article details the theory behind this phenomenon by analyzing the stochastic SIR (Susceptible, Infected, Recovered) model in the cumulative cases domain. We prove that the Markov chain associated with this model reduces, in the ICC plane, to a pure birth chain for the cumulative number of cases, whose limit leads to an independent increments Gaussian process that fluctuates about a deterministic ICC curve. We calculate the associated variance and quantify the additional variability due to estimating incidence over a finite period of time. We also illustrate the universality brought forth by the ICC concept on real-world data for Influenza A and for the COVID-19 outbreak in Arizona.
Immune checkpoint therapy modeling of PD-1/PD-L1 blockades reveals subtle difference in their response dynamics and potential synergy in combination
Authors: Kamran Kaveh, Feng Fu
Date: 2021-03-22 00:00:00

Immune checkpoint therapy is one of the most promising immunotherapeutic methods that are likely able to give rise to durable treatment response for various cancer types. Despite much progress in the past decade, there are still critical open questions with particular regards to quantifying and predicting the efficacy of treatment and potential optimal regimens for combining different immune-checkpoint blockades. To shed light on this issue, here we develop clinically-relevant, dynamical systems models of cancer immunotherapy with a focus on the immune checkpoint PD-1/PD-L1 blockades. Our model allows the acquisition of adaptive immune resistance in the absence of treatment, whereas immune checkpoint blockades can reverse such resistance and boost anti-tumor activities of effector cells. Our numerical analysis predicts that anti-PD-1 agents are commonly less effective than anti-PD-L1 agents for a wide range of model parameters. We also observe that combination treatment of anti-PD-1 and anti-PD-L1 blockades leads to a desirable synergistic effect. Our modeling framework lays the ground for future data-driven analysis on combination therapeutics of immune-checkpoint treatment regimes and thorough investigation of optimized treatment on a patient-by-patient basis.
From viral evolution to spatial contagion: a biologically modulated Hawkes model
Authors: Andrew J. Holbrook, Xiang Ji, Marc A. Suchard
Date: 2021-03-03 00:00:00

Mutations sometimes increase contagiousness for evolving pathogens. During an epidemic, scientists use viral genome data to infer a shared evolutionary history and connect this history to geographic spread. We propose a model that directly relates a pathogen's evolution to its spatial contagion dynamics -- effectively combining the two epidemiological paradigms of phylogenetic inference and self-exciting process modeling -- and apply this \emph{phylogenetic Hawkes process} to a Bayesian analysis of 23,422 viral cases from the 2014-2016 Ebola outbreak in West Africa. The proposed model is able to detect individual viruses with significantly elevated rates of spatiotemporal propagation for a subset of 1,610 samples that provide genome data. Finally, to facilitate model application in big data settings, we develop massively parallel implementations for the gradient and Hessian of the log-likelihood and apply our high performance computing framework within an adaptively preconditioned Hamiltonian Monte Carlo routine.
Seed banks can help to maintain the diversity of interacting phytoplankton species
Authors: Coralie Picoche, Frédéric Barraquand
Date: 2021-03-22 00:00:00
Tasks: Diversity

Seed formation is part of the reproductive cycle, leading to the accumulation of resistance stages that can withstand harsh environmental conditions for long periods of time. At the community level, multiple species with such long-lasting life stages can be more likely to coexist. While the implications of this process for biodiversity have been studied in terrestrial plants, seed banks are usually neglected in phytoplankton multispecies dynamic models, in spite of widespread empirical evidence for such seed banks. In this study, we build a metacommunity model of interacting phytoplankton species, including a resting stage supplying the seed bank. The model is parameterized with empirically-driven growth rate functions and field-based interaction estimates, which include both facilitative and competitive interactions. Exchanges between compartments (coastal pelagic cells, coastal resting cells on the seabed, and open ocean pelagic cells) are controlled by hydrodynamical parameters to which the sensitivity of the model is assessed. We consider two models, i.e., with and without a saturating effect of the interactions on the growth rates. Our results are consistent between models, and show that a seed bank allows to maintain all species in the community over 30 years. Indeed, a fraction of the species are vulnerable to extinction at specific times within the year, but this process is buffered by their survival in their resting stage. We thus highlight the potential role of the seed bank in the recurrent re-invasion of the coastal community, and of coastal environments in re-seeding oceanic regions. Moreover, the seed bank enables populations to tolerate stronger interactions within the community as well as more severe changes to the environment, such as those predicted in a climate change context. Our study therefore shows how resting stages may help phytoplanktonic diversity maintenance.
Vaccine-escape and fast-growing mutations in the United Kingdom, the United States, Singapore, Spain, South Africa, and other COVID-19-devastated countries
Authors: Rui Wang, Jiahui Chen, Kaifu Gao, Guo-Wei Wei
Date: 2021-03-14 00:00:00

Recently, the SARS-CoV-2 variants from the United Kingdom (UK), South Africa, and Brazil have received much attention for their increased infectivity, potentially high virulence, and possible threats to existing vaccines and antibody therapies. The question remains if there are other more infectious variants transmitted around the world. We carry out a large-scale study of 252,874 SARS-CoV-2 genome isolates from patients to identify many other rapidly growing mutations on the spike (S) protein receptor-binding domain (RDB). We reveal that 88 out of 95 significant mutations that were observed more than 10 times strengthen the binding between the RBD and the host angiotensin-converting enzyme 2 (ACE2), indicating the virus evolves toward more infectious variants. In particular, we discover new fast-growing RBD mutations N439K, L452R, S477N, S477R, and N501T that also enhance the RBD and ACE2 binding. We further unveil that mutation N501Y involved in United Kingdom (UK), South Africa, and Brazil variants may moderately weaken the binding between the RBD and many known antibodies, while mutations E484K and K417N found in South Africa and Brazilian variants can potentially disrupt the binding between the RDB and many known antibodies. Among three newly identified fast-growing RBD mutations, L452R, which is now known as part of the California variant B.1.427, and N501T are able to effectively weaken the binding of many known antibodies with the RBD. Finally, we hypothesize that RBD mutations that can simultaneously make SARS-CoV-2 more infectious and disrupt the existing antibodies, called vaccine escape mutations, will pose an imminent threat to the current crop of vaccines. A list of most likely vaccine escape mutations is given, including N501Y, L452R, E484K, N501T, S494P, and K417N.
How will mass-vaccination change COVID-19 lockdown requirements in Australia?
Authors: Cameron Zachreson, Sheryl L. Chang, Oliver M. Cliff, Mikhail Prokopenko
Date: 2021-03-12 00:00:00

Background: To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine. The rest of the population will instead have access to a less effective vaccine. Methods: We apply a large-scale agent-based model of COVID-19 in Australia to investigate the possible implications of this hybrid approach to mass-vaccination. The model is calibrated to recent epidemiological and demographic data available in Australia, and accounts for several components of vaccine efficacy. Findings: Within a feasible range of vaccine efficacy values, our model supports the assertion that complete herd immunity due to vaccination is not likely in the Australian context. For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks. In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43% and 52%, respectively. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. Interpretation: The study presents a strong argument for a large-scale vaccination campaign in Australia, which would substantially reduce both the intensity of future outbreaks and the stringency of non-pharmaceutical interventions required for their suppression.
Quantifying the overall effect of biotic interactions on species distributions along environmental gradients
Authors: Marc Ohlmann, Catherine Matias, Giovanni Poggiato, Stéphane Dray, Wilfried Thuiller, Vincent Miele
Date: 2021-03-18 00:00:00

Separating environmental effects from those of interspecific interactions on species distributions has always been a central objective of community ecology. Despite years of effort in analysing patterns of species co-occurrences and the developments of sophisticated tools, we are still unable to address this major objective. A key reason is that the wealth of ecological knowledge is not sufficiently harnessed in current statistical models, notably the knowledge on interspecific interactions. Here, we develop ELGRIN, a statistical model that simultaneously combines knowledge on interspecific interactions (i.e., the metanetwork), environmental data and species occurrences to tease apart their relative effects on species distributions. Instead of focusing on single effects of pairwise species interactions, which have little sense in complex communities, ELGRIN contrasts the overall effect of species interactions to that of the environment. Using various simulated and empirical data, we demonstrate the suitability of ELGRIN to address the objectives for various types of interspecific interactions like mutualism, competition and trophic interactions. We then apply the model on vertebrate trophic networks in the European Alps to map the effect of biotic interactions on species distributions.Data on ecological networks are everyday increasing and we believe the time is ripe to mobilize these data to better understand biodiversity patterns. ELGRIN provides this opportunity to unravel how interspecific interactions actually influence species distributions.
Assortative clustering in a one-dimensional population with replication strategies
Authors: Sunhee Chae, Nahyeon Lee, Seung Ki Baek, Hyeong-Chai Jeong
Date: 2021-03-14 00:00:00
Tasks: Clustering

In a geographically distributed population, assortative clustering plays an important role in evolution by modifying local environments. To examine its effects in a linear habitat, we consider a one-dimensional grid of cells, where each cell is either empty or occupied by an organism whose replication strategy is genetically inherited to offspring. The strategy determines whether to have offspring in surrounding cells, as a function of the neighborhood configuration. If more than one offspring compete for a cell, then they can be all exterminated due to the cost of conflict depending on environmental conditions. We find that the system is more densely populated in an unfavorable environment than in a favorable one because only the latter has to pay the cost of conflict. This observation agrees reasonably well with a mean-field analysis which takes assortative clustering of strategies into consideration. Our finding suggests a possibility of intrinsic nonlinearity between environmental conditions and population density when an evolutionary process is involved.
Complex ecological communities and the emergence of island species area relationships
Authors: Ankit Vikrant, Martin Nilsson Jacobi
Date: 2021-03-13 00:00:00

It has been a century since the species-area relationship (SAR) was first proposed as a power law to explain how species richness scales with area. There have been many attempts to explain the origin of this predominant form. Apart from the power law, numerous empirical studies also report a semi-log form of the SAR, but very few have addressed its incidence. In this work, we test whether these relationships could emerge from the assembly of large random communities on island-like systems. We reformulate the generalized Lotka-Volterra model by introducing an area parameter that determines the species richness of the assembled communities. Our analysis demonstrates that the two most widely reported relationship forms can emerge due to differences in immigration rates and skewness towards weak interactions. We particularly highlight the incidence of the semi-log SAR for low immigration rates from a source pool, which is consistent with several previous empirical studies. The two SAR forms might show good fits to data over a large span of areas but a power-law overestimates species richness on smaller islands in remote archipelagoes.