Seminars of Theoretical Biology 2022!

May, 25th | 16h30-17h30 | Zoom

Speaker: Quentin Vagne (UniGE)

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Program Spring 2022

11.05.2022 | 16h30-17h30 | Hugo Le Roy (EPFL)
Collective deformation modes promote fibrous self-assembly in protein-like particles

Controlling the self-assembly of particles into an organized structure is a key feature of living organisms as well as a major engineering challenge. In contrast with the ideal case where all particles fit perfectly with each other, we focus on the non-ideal case where the assembling process leads to an elastic deformation of the particles. This non-ideal situation is common for particles that have not been designed to assemble, for example, misfolded proteins that aggregate into fibrils, or erroneous crystallization of medicines. We model the assembly of ill-fitting particles using simple 2D geometrical shapes. We derive an analytical continuum theory of this model that predicts the structure of the resulting aggregates from the mechanical characteristics of the assembling particles. In particular, two mechanical characteristics of the particles are enough to predict the overall aggregate structure. First, when individual particles have soft deformation modes, these modes collectively control the size of large-scale, yet finite, structures. The second characteristic is the incompressibility of the particles which favors anisotropic, hence fibrous aggregates. These predictions obtained with a specific model of particles are then confirmed using particles with randomly generated mechanical properties, showing a surprising consistency with our initial analytical theory.

30.03.2022 | 16h30-17h30 | Daniel Maria Busiello (EPFL)
Nonequilibrium transport phenomena in biochemical systems

Biochemical systems usually operate out of equilibrium, constantly harvesting and consuming energy to maintain a stationary state. Nonequilibrium conditions are always associated with the presence of dissipative fluxes reflecting into the breaking of symmetries that hold at equilibrium. An unbalance in the population of chemical states, and a net transport of molecules are two prominent examples of these emerging phenomena. Thus, when studying biochemical systems, equilibrium expectations might dramatically fail and we need to employ the framework of stochastic thermodynamics. Here, we start considering a simple three-state chemical system, showing that chemical states can be selected through a dissipation-driven mechanism, triggered by diffusive cycles in a thermal gradient. This poses a serious question on how life has originated at its dawn. In this context, nonequilibrium selection of states paves the way for a solution to the furanose conundrum, rooted in the first detailed experimental characterization of D-ribose isomerization. Then, we highlight that this paradigmatic system can exhibit an accumulation of particles on either the cold or warm side, a phenomenon known as thermophoresis. We establish thermophoresis as a genuine nonequilibrium effect, originating from internal microscopic currents consistent with the necessity of transporting heat from warm to cold regions. Finally, transport processes are investigated at the level of a large family of cellular cargos, the ABC transporters. These are ATP-driven machines that can import and export substrates between the cytoplasm and the environment. Their entire phenomenology can be understood by building a model that encapsulates structural biological findings. However, an energetic analysis reveals that ABC transporters are exactly Maxwell Demons, idealized agents that can create concentration gradients with apparently no energy expenditure. Nonequilibrium thermodynamics and information theory unveil the nature of biochemical transporters as energy-consuming and information-processing devices.

30.03.2022 | 16h30-17h30 | Stephan Peischl (UniBern)
When does gene flow facilitate evolutionary rescue?

A major (if subtle) shift in thinking about ecology and evolution over the past generation has been the growing appreciation of how intertwined adaptation and population dynamics are, and how often they proceed on similar time-scales. This new perspective has been recently thrown into high relief by the need to understand how species may respond to environmental change caused by human activity. It seems certain that a substantial fraction of our planet's current biodiversity will be lost to extinction as species' habitats change at an accelerating rate. Some species, however, may be able to escape that fate by adapting, shifting their geographical ranges, or both. This leads to the questions of when, where and how might adaptation allow species to survive, leading to ‘evolutionary rescue’. Some basic answers to those questions come from theory. In this talk I will present recent work on understanding how gene flow, spatial structure and habitat fragmentation affect the probability for evolutionary rescue. I start with a simple analytically tractable model for evolutionary rescue in a two-deme model with gene flow. This model can be analyzed in detail and our main result is a simple condition for when migration facilitates evolutionary rescue, as opposed as no migration. We further investigated the roles of asymmetries in gene flow and/or carrying capacities, and the effects of density regulation and local growth rates on evolutionary rescue. I will also present simulation results of multi-deme models. We find that in many cases spatially structured models can be translated into a simpler island model using an appropriately scaled effective migration rate. Finally, I will discuss continuous space models and highlight analogies to models of evolutionary rescue via modification of the distribution of fitness effects in non-recombining organisms, such as many viral populations.

16.03.2022 | 16h30-17h30 | Hanna ten Brink (EAWAG, UniBern)
Ontogenetic development and the rise and fall of sympatric species diversity

Adaptive radiations, where a lineage diversifies into multiple species exploiting a variety of niches, are responsible for much of the diversity of life. To explain the origins of biodiversity, it is therefore important to understand why some lineages undergo adaptive radiations, whereas others do not. Current theory on the conditions for adaptive radiation has not taken into account the fact that individuals grow during their life and that their ecological role changes profoundly during development. For example, most animal species change their diet during their development. These ontogenetic diet shifts are often accompanied by metamorphosis, where individuals abruptly change their morphology, habitat, and physiology. In my work, I aim to understand the consequences of individual development for the conditions of adaptive radiation. To do so, I make use of size-structured population models, adaptive dynamics, and individual based models. In this talk, I will show that in order to understand the ecological conditions that promote adaptive radiations, it is necessary to take the full life-cycle of individuals into account.

02.03.2022 | 16h30-17h30 | Aurore Picot (CIRB, Collège de France - former UNIL)
Some considerations when modeling context-dependent bacterial interactions

Predicting the dynamics and functions of microbial communities is crucial if we are to control them in applications related to our health or the health of our environment. However, interactions between microbial species depend on the environment and the mechanisms behind the interactions. To disentangle these factors, I will present some results from two projects I worked on during my postdoc in the Mitri lab at UNIL. In the first projet, I fit a model to the growth dynamics of two bacteria in a gradient of toxicity. We characterize the mechanism behind the stress-gradient change of interaction that is experimentally observed and predict the long term dynamics. The second project focuses on the control of microbial communities. When targeting a focal species, the interactions with other members of the community might buffer, amplify, or cancel the objective of the control. Understanding these undesirable indirect effects allows to limit them, which coexistence theory can help with. I will show how the interplay between theory, experiments and data driven modelling helps describing, and controlling the dynamics of microbial communities in which interactions are context-dependent.

Program Autumn 2021

01.12.2021 | 16h30-17h30 | Carlos Melian (EAWAG, UniBe)
Biodiversity patterns in dynamic multiscapes

The latitudinal biodiversity gradient is one the most general patterns in ecology and evolution. Many taxa follow the gradient with a peak in the tropical areas and a decay towards the poles. Deep time and contemporary data suggest the pattern is strongly dependent on the history on Earth both in land- and sea-scapes and many factors come into play to predict biodiversity patterns in space and time. Yet, the existing ecological and eco-evolutionary spatial modeling frameworks lack dynamic scapes processes, i.e., habitat and connectivity changes due to seasonal fluctuations, geodynamics, currents and other factors altering the structure of the scapes that affect biodiversity dynamics. In this talk we will introduce a metacommunity dynamics modeling exercise to show the effect of periodic connectivity changes in the scape on local and regional biodiversity patterns. We show that local and regional diversity can increase with different frequencies and amplitudes of connectivity dynamics, connecting the empirical findings of high biodiversity in both low and highly connected land- and sea-scapes. By contrasting the slope of the empirical relationship between local and regional diversity in marine phytoplankton metacommunities, with the slopes obtained by theoretical simulations, we show how connectivity dynamics affects diversity in the seascape. We will discuss the existing gaps to connect habitat and connectivity dynamics as a framework to unify latitudinal and longitudinal diversity gradients in metacommunities and food webs. Extending metacommunity theory to dynamic habitat and connectivity gradients can provide new testable predictions about species diversity across broad spatiotemporal scales in rapidly changing land- and sea-scapes.

17.11.2021 | 16h30-17h30 | Loïc Marrec (UniBe)
Evolution of antimicrobial resistance in a changing environment

Understanding the evolution of antimicrobial resistance in a host is of paramount importance in addressing this major public health problem. In this presentation, I will focus on the impacts of environmental variability on the evolution of resistance in a microbial population. More specifically, I address these issues by developing stochastic theoretical models and using methods inspired by out-of-equilibrium statistical physics. I will first talk about the emergence of resistance in fixed and variable-size microbial populations undergoing periodic antimicrobial treatments. Then I will discuss the evolutionary rescue by mutants of a microbial population destined for extinction in an environment that progressively degrades, for example in the presence of increasing antimicrobial concentration.

03.11.2021 | 16h30-17h30 | Nirvana Caballero (UniGe)
High-performance computing and disordered elastic systems theory as a framework to study collective cell migration

Controlling cell migration is important in wound healing, infections, morphogenesis, tissue development, and tumor growth. However, control over migrating cells either individually or as a proliferating cell front remains elusive. The plethora of physical and chemical interactions at widely varying length scales present in biological systems leads to highly complex dynamical and geometrical properties.

The statistical physics framework of disordered elastic systems has proven to be successful in unraveling the physical mechanisms behind inert systems with very different microscopical details, including ferromagnets, ferroelectrics, or even earthquakes. In this talk, I will show how this framework is also applicable to the study of growing colonies of epithelial cells and allows us to extract useful information on collective phenomena.

With analytical predictions of the theory of disordered elastic systems and high-performance computation techniques using Graphical Processing Units (GPUs) to analyze billions of points in the colony edges one can demonstrate that interactions at two main lengthscales are responsible for the geometrical properties of proliferating cell fronts: sub-cell lengthscales and at a length scale of 2–10 cells. We find that pharmacological modulation significantly affects the proliferation speed of the cell fronts, and those modulators that promote cell mobility or division also lead to the most rapid evolution of cell front roughness. This approach allows us to show that, contrary to what typical modeling of confluent cell tissues propose, a hierarchy of interactions is necessary to fully capture the behavior of proliferating cell fronts, and mid-lengthscale interactions are particularly important.

20.10.2021 | 16h30-17h30 | Mats Stensrud (EPFL)
Causal inference in medicine and beyond

Many scientific questions involve events that depend on time. For example, diseases develop over time, some workers lose their jobs over time, and mechanical devises are only reliable for certain lengths of time. Scientific questions about time-to-events tend to have a causal objective: Does the drug have an effect on the disease risk? What would happen to unemployment rates if the policy were changed? How can we delay the failure time of the devise?

In this talk, I will use a causal (counterfactual) framework to formally define targets of inference in medicine and biology, where exposures and effects often depend on time. In particular, I will introduce new estimands - the separable effects - for causal inference in settings with competing events. I will explain how the identifying assumptions for these estimand can be evaluated in causal graphs, and I will present three different estimators. As an illustration, I will apply these ideas to analyze data from a randomized clinical trial on chemotherapy in patients with prostate cancer.

06.10.2021 | 16h30-17h30 | Christophe Dessimoz, UNIL/UCL
Laying foundations for very large comparative genomics

Typical comparative genomics analyses consider either single-copy genes across multiple species, or multi-copy genes between pairs of species. Multi-copy genes across multi-species remains challenging conceptually and computationally. Yet genomes are replete with multi-copy genes, and the different copies (called "paralogs") are often associated with different functions.

To address this problem, we have embraced the concept of Hierarchical Orthologous Groups (HOGs). A HOG comprises all the genes that have descended from a single gene across a clade of interest. Hence, HOGs relate present-day genes in terms of their common ancestral genes in key ancestral species.

As I will expose in my talk, the shift from conventional methods to HOGs requires new approaches for inference, benchmarking, visualisation, and integration to downstream analyses—opening up interesting algorithmic and analytical opportunities, and new biological insights on a broad range of questions.

Program Spring 2020/21

12.05.2021 | 16h00-17h00 | Zena Hadjivasiliou, UniGE
The evolution of sexual differentiation

The two partners required for sexual reproduction are rarely the same. This pattern extends to species which lack sexual dimorphism yet possess self-incompatible gametes determined at mating-type regions of suppressed recombination, likely precursors of sex chromosomes. In this seminar I will argue that the interactions between cells required for sexual fusion are optimized when cells communicate asymmetrically. I will show that under selection for robust signaling between mating cells and fast mating, cells that belong to different mating type groups evolve. I will discuss how these findings can help explain other key features of the sexual lives of unicellular organisms like the number of mating types, the ability of some cells to switch their mating type identity stochastically and the emergence of sex chromosomes.

28.04.2021 | 16h00-17h00 | Anne-Florence Bitbol, EPFL
Impact of population spatial structure on mutant fixation probabilities, from models on graphs to the gut

Microbial populations often have complex spatial structures, with homogeneous competition holding only at a local scale. Population structure can strongly impact evolution, in particular by affecting the fixation probability of mutants. I will first discuss a general model for describing structured populations on graphs. Then I will show that the specific structure of the gut with gradients of food and bacterial concentrations can increase the fixation probability of neutral mutants, which can have consequences for the diversity of the microbiota.

13.04.2021 | 16h00-17h00 | Chan Cao, EPFL
Single-molecule sensing with biological nanopore

Nanopore sensing is an electrophoretic approach that can detect a single molecule as it transports through a pore of nanometer scale. The resulting electric current signal is exquisitely sensitive to the molecule of interest and thus can provide information about molecular size, mass, charge, composition, structure and conformation in real-time. Biological nanopores have been successfully applied in DNA sequencing with some unique features, long reads, low cost, high speed and a minimal sample preparation. Recently, this success has inspired its application for protein sensing and sequencing. As nanopore technology has been widely applied in many fields, there is a need for exploring novel biological nanopore candidates and improving them through molecular engineering. We have recently rationally designed and evaluated a set of mutated pores in silico by molecular dynamic simulations and in vitro by single-channel recording to study the structure–function relationship between the biological nanopore and its molecular sensing properties. Thanks to these fundamental understandings, we could precisely engineer the diameter and electrostatic properties of the nanopore, thereby achieving a more accurate molecular detection for DNA and peptide. Exploiting these engineered biological nanopores, we have applied nanopore approach in a frontier project: decoding digital information stored in tailored macromolecules. This study opens promising possibilities to develop writing-reading techniques to process digital data using a biologically-inspired platform, providing an alternative solution for future data storage.

31.03.2021 | 16h00-17h00 | Piret Avila, UNIL
Hamilton’s rule meets the Hamiltonian and state-feedback effects: gradual evolution of phenotypically plastic traits using optimal control theory

Most traits expressed by organisms, such as gene expression profiles, developmental trajectories, behavioural sequences and reaction norms are function-valued traits (colloquially "phenotypically plastic traits"), since they vary across an individual's age and in response to various internal and/or external factors (state variables). Furthermore, most organisms live in populations subject to limited genetic mixing and are thus likely to interact with their relatives. We here formalise selection on genetically determined function-valued traits of individuals interacting in a group-structured population, by deriving the marginal version of Hamilton's rule for function-valued traits. This rule simultaneously gives a condition for the invasion of an initially rare mutant function-valued trait and its ultimate fixation in the population (invasion thus implies substitution). Hamilton's rule thus underlies the gradual evolution of function-valued traits and gives rise to necessary first-order conditions for their uninvadability (evolutionary stability). We develop a novel analysis using optimal control theory and differential game theory, to simultaneously characterise and compare the first-order conditions of (i) open-loop traits - functions of time (or age) only, and (ii) closed-loop (state-feedback) traits - functions of both time and state variables. We show that closed-loop traits can be represented as the simpler open-loop traits when individuals do not interact or when they interact with clonal relatives. Our analysis delineates the role of state-dependence and interdependence between individuals for trait evolution, which has implications to both life-history theory and social evolution.

17.03.2021 | 16h00-17h00 | Xiang-Yi Li, UniNe
Applications of evolutionary game theory in understanding biological interactions

Evolutionary game theory is a very useful tool to solve problems where the outcome of adopting a given strategy depends on the strategies adopted by others. A simple example is a children’s rock-paper-scissors game, where there is there no obvious “best option” for an individual to choose. It has become an integral part of modern evolutionary theory, and has expanded in many directions and melded with other methods. In this talk, I will present a quick introduction to evolutionary game theory, and a few case studies based on my own research to show how we can use evolutionary game theory to better understand complex biological interactions, such as the “battle of the sexes” and the competition between different bacteria. We will cover diverse topics such as female preference for fancy males escalating into a “tragedy of the commons”, leading to reduced population growth rate (or even extinction); male offspring evolve to “altruistically” disperse early away from their natal habitat to leave more resources for their sisters; and why some males continue to provide intensive care to the offspring despite being cheated by their females; and how a slowly growing bacterium use a temperate phage as weapon against its fast growing competitor.

03.03.2021 | 16h00-17h00 | Helena Todorov, UNIL
Structure learning to unravel mechanisms of the immune system

Our body constantly has to defend itself against harmful pathogens. Luckily, we have a powerful protective mechanism called the immune system. The immune system is at the center of extensive studies, facilitated by the appearance of new technologies that allow to measure unprecedented amounts of features in thousands to millions of cells. This leads to large-scale, high-dimensional data that typically contain many sources of variability. New automated tools are therefore needed to analyse this type of complex data, and to extract interesting patterns from it. We applied and designed various structure learning methods to gain insight into the complex nature of immune cell differentiation in response to a disease. We extracted cell trajectories, gene regulatory networks, and graphs of interacting molecules that helped us to generate new medical hypotheses. The types of machine learning tools that we applied represent a real asset in the analysis of complex data and help to shed light on the immune system’s response to diseases that are still difficult to characterise.

Program Fall 2020/21

02.12.2020 | 16h00-17h00 | Sven Bergmann, UNIL
GWAS meets networks: Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

19.11.2020 | 16h00-17h00 | Charles Mullon, UNIL
Eco-evolutionary dynamics under non-random interactions

Organisms continuously modify their living conditions, transforming their environment, microbiome, and sometimes culture. Where these modifications influence the fitness of conspecifics, a feedback emerges between the evolution of traits and the environment in which they are expressed. To investigate such feedback, it is typically assumed that individuals interact at random. In this case, one can study the invasion of a rare mutant trait in an environment set by a common resident ignoring mutant-mutant interactions. However, non-random interactions are common in nature. In this talk, I will report some results on the effect of non-random interactions on eco-evolutionary dynamics, focusing on two mechanisms that lead to such non-random interactions: spatial structure and biased behaviours between parents and their offspring. In both cases, selection depends on complex feedbacks between individuals of the same mutant lineage. By disentangling and quantifying these feedbacks, this research can help understand the nature of adaptation via non-genetic modifications, with implications for how organisms evolve to transform their environments.

04.11.2020 | 16h00-17h00 | Christian Mazza, UNFR
Self-organization and pattern formation in auxin flow

The plant hormone auxin plays a central role in growth and morphogenesis. In the shoot apical meristem, the auxin flow is polarized through its interplay with PIN proteins. Concentration based mathematical models of the auxin flow permit to explain some aspects of phyllotaxis, where auxin accumulation points act as auxin sinks and correspond to primordia. Simulations show that these models can reproduce geometrically regular patterns like spirals in sunflowers or Fibonacci numbers. We propose a mathematical study of a related non-linear o.d.e. using tools from Markov chain theory. We next consider a concurrent model which is based on the so-called canalization hypothesis, and show that it can explain the self-organization of plant vascular systems.

22.10.2020 | 16h00-17h00 | Sara Mitri, UNIL
Combining theory and experiments in microbial ecology and evolution

Our lab strives to understand the ecology and evolution of small microbial communities and to control and design them to our benefit. This interest stems both from their practical importance - microbial communities affect our health and are heavily used in food production, environmental remediation and agriculture - and their advantages as model systems to ask fundamental questions about the interplay between ecology and evolution. To reach this understanding, we combine experiments with mathematical and computational models. In this talk, I will give an overview of this research, focusing on the strengths and challenges of an interdisciplinary approach.

08.10.2020 | 16h00-17h00 | Michel Benaïm, UNINE
Stochastic persistence

An important issue in mathematical ecology and population biology is to find out under which conditions a collection of interacting species can coexist over long periods of time. A similar question, in mathematical models of disease dynamics, is to understand whether or not a disease will be endemic (i.e persist in the population) or go extinct. The mathematical investigation of these types of questions began in the late 1970s, laying the foundation of what is now called the (deterministic) mathematical theory of persistence. The theory developed rapidly the past 35 years using the available tools from dynamical system theory. Beside biotic interactions, environmental fluctuations play a key role in population dynamics. In order to take into account these fluctuations and to understand how they may affect persistencedeterministic models need to be replaced by stochastic ones and the theory needs to be revisited. This talk will survey recent results in this direction laying the groundwork for a mathematical theory of stochastic persistence. Part of this work stems from a close collaboration between Neuchatel’s research group in probability and UC Davis department of Evolution and Ecology.

Kick-off of the Network of Theoretical Biology in Western Switzerland

November 26 2019 |15h-19h | Biophore | UNIL


Rachel Jeitziner, SIB
Two-Tier Mapper: a topological tool for the analysis of biological data

Tadeas Priklopil, UNIL
Modelling evolution in structured populations

Organizing team of kick-off event

Piret Avila, UNIL
Stefania Ebli, EPFL
Daniela Egas, EPFL
Isaline Guex, UNIFR
Zena Hadjivasiliou, UNIGE
Joseph Lemaître, EPFL

Xiang-Yi Li, UNINE
Christian Mazza, UNIFR
Jose Negrete, EPFL
Xavier Richard, UNIFR
Pauline Ruegg-Reymond, EPFL
Björn Vessman, UNIL