Wednesday, August 21 |
07:30 - 09:00 |
Breakfast (Restaurant at your assigned hotel) |
09:30 - 10:00 |
Paul Francois: Attack and defence in cellular decision-making: lessons from machine learning ↓ Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and models of cellular decision-making relevant for immunology. We apply attacks from machine learning to simple decision-making models, and show explicitly the correspondence to antagonism by weakly bound ligands. We then apply a gradient-descent approach from machine learning, similar to an evolving agent trying to fool the cellular decision system. We reveal the existence of two regimes characterized by the presence or absence of a critical point for the gradient. This causes the strongest antagonists to lie close to the decision boundary. Our work connects evolved cellular decision-making to machine learning, and motivates the design of a general theory of adversarial perturbations, both for in vivo and in silico systems. (Conference Room San Felipe) |
10:10 - 10:40 |
Simone Pigolotti: Bet-hedging strategies in expanding populations ↓ Ecological species can spread their extinction risk in an uncertain environment by adopting a bet-hedging strategy, i.e, by diversifying individual phenotypes. I will present a theory of bet-hedging for populations colonizing an unknown environment that fluctuates either in space or time. We find that diversification is more favorable for range expansion than in the well-mixed case, supporting the view that range expansions promote diversification. For slow rates of variation, spatial fluctuations open more opportunities for bet-hedging than temporal variations. Opportunities for bet-hedging reduce in the limit of frequent environmental variations. These conclusions are robust against demographic stochasticity induced by finite population sizes.
Ref. P. Villa-Martin, M.A. Muñoz, S. Pigolotti, Plos Comp. Biol. 15(4): e1006529 (2019).
(Conference Room San Felipe) |
10:40 - 11:10 |
Coffee Break (Conference Room San Felipe) |
11:10 - 11:40 |
Luca Peliti: Transient compartmentalization and its associated error thresholds ↓ A recently proposed mechanism suggests that transient compartmentalization could have preceded cell division in prebiotic scenarios. Here, we study various classes of transient compartmentalization dynamics. We show that two regimes are possible: In a diffusion-limited regime (e.g. simple autocatalysis), a large noise is generated at the population level due to asynchronous growth. In contrast, in a replication-limited regime with many steps (e.g. polymerization), a low noise is generated at the population level. Since strong noise will yield many unviable population compositions, polymerization can present a strong fitness advantage. For deterministic growth dynamics, we introduce mutations that turn functional replicators into parasites. This can either lead to coexistence or parasite dominance, and we derive the phase boundary separating these two phases as a function of relative growth, inoculation size and mutation rate. We show that transient compartmentalization allows coexistence beyond the classical error threshold. (Conference Room San Felipe) |
11:45 - 12:15 |
Jakub Otwinowski: Phenotype evolution as optimization ↓ Biological evolution can be described as a population climbing a fitness landscape, and has inspired a variety of derivative-free optimization algorithms. Here we describe how phenotype evolution has sophisticated optimization properties. In particular, natural selection approximates second order gradient descent (Newton's method), and recombination is efficient in generating diversity. We use these insights to design a new type of derivative-free optimization algorithm for continuous problems. (Conference Room San Felipe) |
12:20 - 12:50 |
Arvind Murugan: Physical constraints on epistasis in proteins and gene regulatory networks ↓ Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we argue that the physical properties of a biological system can generically and easily constrain its epistasis. We analyze the interaction between mutations in generic models of proteins and biochemical networks. In each case, a slow, collective physical mode is actuated upon mutation, reducing the dimensionality of mutational effects and thus the rank of the epistatic matrix. This, in turn, reduces the ruggedness of the sequence-to-function map. By providing a mechanistic basis for experimentally observed global epistasis, these results suggest that slow collective physical modes can make biological systems more evolvable. (Conference Room San Felipe) |
12:55 - 13:25 |
Alexandre Morozov: A biophysical model of codon bias evolution ↓ Frequencies of synonymous codons are typically non-uniform, despite the fact that such codons correspond to the same amino acid in the genetic code. This phenomenon, known as codon bias, is broadly believed to be due to a combination of factors including genetic drift, mutational effects, and selection for speed and accuracy of codon translation; however, quantitative modeling of codon bias has been somewhat elusive. I will present a biophysical model which explains genome-wide codon frequencies observed across 20 organisms. Our model implements detailed codon-level treatment of mutations and includes two contributions to codon fitness which describe codon translation speed and accuracy. We find that the observed patterns of genome-wide codon usage are consistent with a strong selective penalty for mistranslated amino acids, while the dependence of codon fitness on translation speed is much weaker on average. Treating the translation process explicitly in the context of a finite ribosomal pool has allowed us to highlight the biophysical underpinnings of codon-level selective pressures. Overall, our approach offers a unified biophysical and population genetics framework for understanding the origin of codon bias. (Conference Room San Felipe) |
13:30 - 15:00 |
Lunch (Restaurant Hotel Hacienda Los Laureles) |
15:00 - 15:30 |
Coffee Break (Conference Room San Felipe) |
15:30 - 16:00 |
Benjamin Good: Ecological and evolutionary responses of the human gut microbiome during antibiotic treatment ↓ Species level data show that gut microbiota can be remarkably resilient to brief environmental perturbations like antibiotics. However, little is currently known about how this ecological robustness is implemented at the strain level. In this talk, I will describe our recent efforts to address this question, by analyzing longitudinally sampled metagenomes from a single antibiotic-treated individual over a six-month period. We have developed new statistical approaches to simultaneously measure the ecological and evolutionary dynamics across multiple species in this community during the course of antibiotic treatment, revealing dramatic shifts in the genetic composition of individual species. I will show how these data can help distinguish between possible mechanisms of resilience, and what they can teach us about the population genetic forces that act within this community. (Conference Room San Felipe) |
16:05 - 16:35 |
Anton Zilman (Conference Room San Felipe) |
16:40 - 17:10 |
Ivana Bozic: Evolutionary dynamics of cancer and its response to treatment ↓ Cancer is the result of a stochastic evolutionary process characterized by the accumulation of mutations that are responsible for tumor growth, immune escape, and drug resistance, as well as mutations with no effect on the phenotype. Stochastic modeling can be used to describe the dynamics of tumor cell populations and to obtain insights into the hidden evolutionary processes leading to cancer. I will present recent approaches that use branching process models of cancer evolution to quantify intra-tumor heterogeneity and the development of drug resistance, and their implications for interpretation of cancer sequencing data and the design of optimal treatment strategies. (Conference Room San Felipe) |
19:00 - 21:00 |
Dinner (Restaurant Hotel Hacienda Los Laureles) |