Evolution of biological cooperation: An algorithmic approach

This manuscript presents an algorithmic approach to cooperation in biological systems, drawing on fundamental ideas from statistical mechanics and probability theory. Fisher’s geometric model of adaptation suggests that the evolution of organisms well adapted to multiple constraints comes at a significant complexity cost. By utilizing combinatorial models of fitness, we demonstrate that the probability of adapting to all constraints decreases exponentially with the number of constraints, thereby generalizing Fisher’s result. Our main focus is understanding how cooperation can overcome this adaptivity barrier. Through these combinatorial models, we demonstrate that when an organism needs to adapt to a multitude of environmental variables, division of labor emerges as the only viable evolutionary strategy

Biome stability and fragmentation under critical environmental temperature change

Critical phenomena in the climate system can cause drastic changes in the state of planetary ecosystems as well the entire biosphere. There also are mechanisms through which the biosphere can make an effect on climate. In this manuscript, we study the nonlinear dynamics of the interaction of the climate system with the biosphere by linking an energy balance climate model to different species competition models. We develop an asymptotic approach to these models and investigate how migration strengthens biome stability and biodiversity. Moreover, we derive relations describing biome boundary shifts under global warming (or cooling) and check those relations against paleo data on plant biome location. Finally, the models demonstrate that critical rates of changes in the environmental temperature dynamics may shift biome stability

The influence of environmental forcing on biodiversity and extinction in a resource competition model

In this paper, we study a model of many species that compete, directly or indirectly, for a pool of common resources under the influence of periodic, stochastic, and/or chaotic environmental forcing. Using numerical simulations, we find the number and sequence of species going extinct when the community is initially packed with a large number of species of random initial densities. Thereby, any species with a density below a given threshold is regarded to be extinct

Sub-surface geology and velocity structure of the Krafla high temperature geothermal field, Iceland : Integrated ditch cuttings, wireline and zero offset vertical seismic profile analysis

The research leading to these results has received funding from the European Community's Seventh Framework Programme under grant agreement No. 608553 (Project IMAGE). The VMAPP project run by VBPR, DougalEARTH Ltd. and TGS also contributed funding to the borehole characterization of the K-18 borehole. Landsvirkun is acknowledged for their effort and assistance in this work and in particular for allowing the use of the data from well K-18. We further acknowledge the support from the Research Council of Norway through its Centres of Excellence funding scheme, project 22372 (SP and DAJ).Peer reviewedPostprin

A mathematical model for positive permafrost carbon-climate feedback

The permafrost methane emission problem is the focus of attention on different climate models. Here, we present a mathematical model for permafrost lake methane emission and its influence on the climate system. We model this process using the theory of non-linear phase transitions. Further, we find that a climate catastrophe possibility depends on a value of feedback connecting the methane concentration in the atmosphere and temperature, and on the tundra permafrost methane pool.We note that the permafrost lake model that we developed for the methane emission positive feedback loop problem is a conceptual climate model

Algorithms and complexity in biological pattern formation problems

International audienceIn this paper we develop a new mathematical approach to the pattern formation problem in biology. This problem was first posed mathematically by A.M. Turing, however some principal questions were left open (for example, whether there exists a "universal" mathematical model that allows one to obtain any spatio-temporal patterns). Here we consider the pattern formation ability of some class of genetic circuits. First, we show that the genetic circuits are capable of generating arbitrary spatio-temporal patterns. Second, we give upper and lower bounds on the number of genes in a circuit generating a given pattern. A connection between the complexity of gene interaction and the pattern complexity is found. We investigate the stochastic stability of patterning algorithms