Name: From Molecular Basis to Predictability and Control of Evolution
Start: 2019-07-01T09:00:00+02:00
End: 2019-07-26T18:00:00+02:00
Location: Nordita, Stockholm

Monday, 1 July 2019

09:15 Welcome
Welcome
09:15 - 09:30
Room: FB52
09:30 TBA - Dan Andersson
TBA
- Dan Andersson
09:30 - 10:30
Room: FB52
11:00 TBA - Michael Laessig
TBA
- Michael Laessig
11:00 - 12:00
Room: FB52
Tuesday, 2 July 2019
09:30 Impact of a periodic presence of antimicrobial on resistance evolution in a microbial population - Anne-Florence Bitbol
Impact of a periodic presence of antimicrobial on resistance evolution in a microbial population
- Anne-Florence Bitbol
09:30 - 10:30
Room: FB52 Antimicrobial treatments select for resistance, and once resistance becomes widespread, antimicrobials become useless. Resistance evolution within a host can be strongly affected by variations of antimicrobial concentration that may exist e.g. during a treatment. We are investigating these effects using stochastic models. First, in a microbial population of fixed size, we showed that fast alternations of phases with and without antimicrobial strongly accelerate the evolution of resistance, especially for large populations. Next, we considered microbial populations of variable size, in which we studied the impact of biocidal drugs, that kill microorganisms, and of biostatic drugs, that prevent microorganisms from growing. In both cases, we showed that the probability of treatment success, i.e. of extinction of the microbial population, strongly depends on the period of the alternations of drug absence and presence. Moreover, we showed that biocidal antimicrobials promote resistance more than biostatic ones. Finally, we found a population size-dependent critical drug concentration below which antimicrobials cannot eradicate microbial populations.
11:00 How adaptive immunity governs co-evolution in microbes - Sidhartha Goyal
How adaptive immunity governs co-evolution in microbes
- Sidhartha Goyal
11:00 - 12:00
Room: FB52 Features of the CRISPR-Cas system, in which bacteria integrate small segments of phage genome (spacers) into their DNA to neutralize future attacks, suggest that its effect is not limited to individual bacteria but may control the fate and structure of whole populations [1]. In our model, we find that early dynamics of a recently acquired spacer is largely independent of phage dynamics but crucially depends on the burst-size of phage infections. In contrast, the fates of high abundant spacers are strongly influenced by the feedback from phages that creates a time-dependent fitness landscape for the spacers. Taken together, we quantify the role of population parameters in maintaining phage and bacterial diversity where CRISPR-cas is in the play. [1] https://www.pnas.org/content/115/32/E7462)
14:00 A statistical-inference approach to access an ‘interaction’ pattern among migrating cells - Elena Agliari
A statistical-inference approach to access an ‘interaction’ pattern among migrating cells
- Elena Agliari
14:00 - 14:30
Room: FB52 In this talk I will discuss the development and implementation of a statistical-inference tool to investigate extracellular and contact independent cell-to-cell communication, that possibly yield to collective migration. This tool, based on the maximum-entropy principle, is entirely statistically-driven rather than biologically-driven: it does not rely on an a priori knowledge of the biochemical interactions among the migrating cells. Instead, we leverage the statistical information stored in empirical observations on cell motility, e.g., quantitative analyses of cell speed and directionality obtained in control environments, such as microfluidic devices. I will finally present some examples of application, where we focus on immune cells moving on a lab-on-chip under different conditions. References E. Agliari, E. Biselli, A. De Ninno, G. Schiavoni, L. Gabriele, et al., “Cancer-driven dynamics of immune cells in a microfluidic environment”, Scientific Reports, 4, 6639 (2014). E. Biselli, E. Agliari, A. Barra, F.R. Bertani, A. Gerardino, et al., “Organs on chio approach: a tool to evaluate cancer-immune cells interactions”, Scientific Reports, 7, 12737 (2017). E. Agliari, P.J. Sáez, A. Barra, M. Piel, P. Vargas, M. Castellana, “A maximum-entropy approach for leukocyte migration in lab-on-chip experiments”, to be submitted.
14:30 An uncertainty law for microbial evolution - Kristina Krona
An uncertainty law for microbial evolution
- Kristina Krona
14:30 - 15:00
Room: FB52 Higher order epistasis is of practical importance for antibiotic and HIV drug resistance. For instance, a triple mutant may lead to treatment failure, whereas the corresponding single and double mutant are harmless. The relative importance of pairwise versus higher order interactions have been subject to some debate. However recent empirical studies suggest that the latter type is important. We explored the relation between higher order epistasis and predictability by considering fitness graphs, i.e., directed hyper-cube graphs. For each one of the 193,270,310 four-cube graphs, we counted the peaks and checked if the graph implied epistasis by using our characterization of graphs with this property. We found a strong association between higher order epistasis and the number of peaks based on exact computation. The relevance for larger system, as well as for antimicrobial drug resistance, will be discussed.
Wednesday, 3 July 2019
09:30 Forecasting experimental evolution in Pseudomonas - Peter Lind
Forecasting experimental evolution in Pseudomonas
- Peter Lind
09:30 - 10:30
Room: FB52 Previous work on the adaptive “wrinkly spreader” (WS) type in Pseudomonas fluorescens allowed us to develop models and rules to predict evolutionary outcomes. Knowledge of the genotype-to- phenotype map of the WS phenotype and of alternative phenotypic solutions to the problem of air-liquid interface colonization allows forecasting for related species. Equipped with the genome sequences of P. protegens and P. syringae combined with knowledge from work in P. fluorescens, predictions about the outcome of experimental evolution were made. Phenotypic predictions were successful in terms of the exopolymeric substances (EPSs) structural components used to colonize the air-liquid interface caused by mutational activation of diaguanylate cyclases (DGCs). As predicted, the most common mutation type was loss-of-function mutation followed by less common promoter mutations and promoter capture events. Although up to 39 different DGCs are encoded in these genomes the relative rates of the top two or four was successfully predicted. Individual mutations could not be predicted, but the mutated regions were conserved between species with the majority of mutations expected to disrupt intermolecular or interdomain interactions. Mutational hot spots were not conserved between species. This work shows the potential of extending one of the best- characterized experimental evolution systems to other species allowing true testing of evolutionary forecasts. Frequent horizontal gene transfer and gene loss of DGCs and genes encoding structural EPSs creates a large diversity among different Pseudomonas species meaning that the prediction is different for each species. This allows an iterative workflow of prediction, experimental evolution and model improvement to explore the limits of evolutionary forecasting. There is potential for collaborations on many levels including development and testing of methods for predictions on different biological levels, theoretical considerations and modelling as well as comparisons with other experimental systems.
11:00 Repeatability and predictability in microbial evolution - Richard Neher
Repeatability and predictability in microbial evolution
- Richard Neher
11:00 - 12:00
Room: FB52 RNA viruses like HIV of influenza virus evolve rapidly and thereby evade human immunity. While molecular evolution proceeds in the high dimensional space of possible genomes, independent realization of the viral evolution frequently proceed via similar mutational patterns and predictably revert to ancestral states once selection pressures subside. I will discuss the extent and limit of such repeatability using longitudinal deep sequencing data of HIV populations. Related patterns can be used to predict which influenza virus variants are most likely to succeed and circulate in future seasons. In bacteria repeatability to is typically limited to very strong pressure such as antibiotic selection and is often found at the level of genes and pathways rather than individual positions in the genome.
Thursday, 4 July 2019
09:30 Population size and the predictability of antibiotic resistance evolution - Arjan de Visser
Population size and the predictability of antibiotic resistance evolution
- Arjan de Visser
09:30 - 10:30
Room: FB52 Although evolution is inherently stochastic due to chance events, such as mutations, it is not fully random due to deterministic consequences of natural selection and constraints imposed by the fitness landscape. A better understanding of the causes and constraints of evolution will help to control the evolution of unwanted phenotypes, such as antibiotic-resistant pathogens. I will present results from evolution experiments with the notorious antibiotic resistance enzyme, TEM-1 beta lactamase, in the presence of a novel antibiotic. We use both in vitro (only TEM) and in vivo (TEM + host) evolution experiments to address the effect of population size on the tempo and mode of adaptation. The results from in vitro experiments show greater adaptive heterogeneity and surprisingly higher resistance in small than in large populations, reflecting the rugged fitness landscape of the enzyme. In the in vivo experiments, we find no adaptive benefit for small populations – suggesting a more smooth adaptive landscape, but differential effects from population size on the repeatability of different mutation classes: large populations show more parallel SNPs, while small populations show more parallel large genomic deletions and duplications. We are testing the hypothesis that this divergent pattern of parallel evolution derives from clonal interference benefitting lower- rate, but larger-benefit SNPs in populations large enough for both classes of mutations to occur.
11:00 TBA - Fernanda Pinheiro
TBA
- Fernanda Pinheiro
11:00 - 12:00
Room: FB52
14:00 Toy models of evolved adapting machines - Matteo Marsili
Toy models of evolved adapting machines
- Matteo Marsili
14:00 - 14:30
Room: FB52 The internal state of living systems, from single proteins to whole cells and organisms, needs to respond to the state of their environment. At odds with physical systems, the Hamiltonian of living systems is shaped by evolution in order to make this interaction optimal. Within a highly stylized model, I shall describe systems whose internal state is maximally informative about the state of the environment, and compare them to normal physical systems. (see https://arxiv.org/abs/1904.09144).
14:30 Some recent progresses in Neural Networks & Machine Learning via Statistical Mechanics - Adriano Barra
Some recent progresses in Neural Networks & Machine Learning via Statistical Mechanics
- Adriano Barra
14:30 - 15:00
Room: FB52 In this talk, keeping a statistical mechanical perspective, I will first revise the link between learning in Restricted Boltzmann Machines and retrieval in Hopfield Neural Networks. Then, I will focus on the importance in the choice of the nature of both neurons and synapses (e.g. digital vs analog) for both learning and retrieval mechanisms. Finally, I will discuss some variants of the Hebbian paradigm that allow the network to saturate the critical storage capacity, reaching Gardner's bound for symmetric networks. Minimal Reference: [1] Barra, A., Genovese, G., Sollich, P., & Tantari, D. (2018). Phase diagram of restricted Boltzmann machines and generalized Hopfield networks with arbitrary priors. Physical Review E, 97(2), 022310. [2] Fachechi, A., Agliari, E., & Barra, A. (2019). Dreaming neural networks: forgetting spurious memories and reinforcing pure ones. Neural Networks. [3] Agliari, E., Barra, A., Galluzzi, A., Guerra, F., & Moauro, F. (2012). Multitasking associative networks. Physical review letters, 109(26), 268101.
15:15 Poster Flash Talks W1
Poster Flash Talks W1
15:15 - 16:00
Room: FB52

16:00 Poster Session W1
Poster Session W1
16:00 - 19:00
Room: FB52
Friday, 5 July 2019
09:30 Antigenic evolution of viruses in host populations - Igor Rouzine
Antigenic evolution of viruses in host populations
- Igor Rouzine
09:30 - 10:30
Room: FB52 To escape immune recognition in previously infected hosts, viruses evolve genetically in immunologically important regions. The host’s immune system responds by generating new memory cells recognizing the mutated viral strains. Despite recent advances in data collection and analysis, it remains conceptually unclear how epidemiology, immune response, and evolutionary factors interact to produce the observed speed of evolution and the incidence of infection. Here we establish a general and simple relationship between long-term cross- immunity, genetic diversity, speed of evolution, and incidence. We develop an analytic method fusing the standard epidemiological susceptible-infected-recovered approach and the modern virus evolution theory. The model includes the factors of strain selection due to immune memory cells, random genetic drift, and clonal interference effects. We predict that the distribution of recovered individuals in memory serotypes creates a moving fitness landscape for the circulating strains which drives antigenic escape. The fitness slope (effective selection coefficient) is proportional to the reproductive number in the absence of immunity R0 and inversely proportional to the cross-immunity distance a, defined as the genetic distance of a virus strain from a previously infecting strain conferring 50% decrease in infection probability. Analysis predicts that the evolution rate increases linearly with the fitness slope and logarithmically with the genomic mutation rate and the host population size. Fitting our analytic model to data obtained for influenza A H3N2 and H1N1, we predict the annual infection incidence within a previously estimated range, (4-7)%, and the antigenic mutation rate of Ub = (5 − 8) 10−4 per transmission event per genome. Our prediction of the cross- immunity distance of a = (14 − 15) aminoacid substitutions agrees with independent data for equine influenza. Importantly, we demonstrate that variation in basic reproduction ratio R0 is mostly responsible for the variation among strains, and explain the observed inverse correlation between the substitution rate and the time to the most recent common ancestor.
11:00 Implications of HA protein stability on influenza A virus evolution - Roy Chadi
Implications of HA protein stability on influenza A virus evolution
- Roy Chadi
11:00 - 11:30
Room: FB52 Thanks to their rapid evolution, influenza A viruses are able to seasonally infect a significant fraction of the human population. This evolution is driven by strong immune selection in partially immunized hosts, coupled with an error-prone RNA polymerase. The hemagglutinin (HA), a key viral surface protein, is the major target of host antibodies and thus the site of most variation. How do functional constraints of the HA, such as protein stability, shape evolutionary and transmission dynamics? In this work, we formulate a cross-scale mathematical model that considers molecular properties of the HA, within-host evolutionary dynamics, and between-host transmission. We characterize the emerging properties of flu, and glean important insight into its long-term evolution.
Saturday, 6 July 2019
Sunday, 7 July 2019