Our adaptive immune system is able to learn from past experiences to better fit an unforeseen future. This is made possible by a diverse and dynamic repertoire of cells expressing unique antigen receptors on their surface and capable of rapid evolution within an individual. However, naturally occurring immune response exhibits limits in efficacy, speed and capacity to adapt to novel challenges. In this talk, I will discuss theoretical frameworks we developed to (1) explore functional impacts of non-equilibrium antigen recognition, and (2) identify conditions under which natural selection acting local in time can find adaptable solutions favorable in the long run. Using these examples, I show that a generalized landscape theory provides a unifying framework for connecting physical mechanisms and evolved functions. To close, I briefly discuss the need for considering viral-immune ecology to gain a fuller understanding of their coevolution.

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It is well-known that there are interactions everywhere in the living world. This seems very much like old scientific news. However, I will argue that, when biological evolution is investigated in ways that focus on microbes and interactions, some rather unexpected, evolutionary meaningful, interactions can still be uncovered. In this talk, I will introduce some recently published works from my lab and go through three, quite distinct cases of such surprising interactions. First, I will show how the use of similarity networks sheds light on a diversity of remodelled genes (symbiogenetic, chimeric, etc.), which arise from interactions between molecular components within genomes. Then, I will describe unexpected interactions between phages and bacteria, suggested by the prediction of novel quorum-sensing systems. Finally, I will provide original evidence that protein-protein interaction networks associated with ageing in humans are possibly impacted by ‘age-distorters’, suggesting that dozens of viruses can alter or manipulate human ageing. I will explain why such findings expand current evolutionary studies of ageing and encourage the development of original co-evolutionary analyses of ageing.

Due to the advent of high-throughput technologies, high-dimensional “omics” data are produced at an increasing pace. In cancer biology, national and international consortia have profiled thousands of tumors at multiple molecular levels (“multi-omics”) allowing to gather a comprehensive molecular picture of this disease. Moreover, multi-omics profiling approaches are currently being transposed at single-cell resolution, further increasing the information accessible from cancer samples. The current main challenge is to design appropriate methods to integrate this wealth of information and translate it into actionable biological knowledge. In this talk, I will discuss two maincomputational directions, both grounded on multi-view learning: (i) multilayer networks to integrate a large range of interactions and (ii) dimensionality reduction to extract biological knowledge simultaneously from multiple omics. First, I will present the development done by my team for  bulk data and then our ongoing research in single-cell.

Paramecium is a unicellular organism that swims in fresh water using cilia. When it is stimulated (mechanically, chemically, optically, thermally, etc.), it often swims backward then turns and swims forward again: this is called the avoiding reaction. This reaction is triggered by a calcium-based action potential. For this reason, it enjoyed a period of glory in the 1970s as a model organism for neuroscience – some authors called it the “swimming neuron”. Paramecium has considerable experimental advantages: it can be easily mass cultured in days, it is accessible to intracellular electrophysiology, its genome has been sequenced and it benefits from many techniques such as RNA interference. Because Paramecium is both a cell and an organism, it becomes possible to develop quantitative models that link physiology and behavior, cellular plasticity and behavioral plasticity (including learning), theoretical neuroscience and systems biology. I will describe our first efforts to develop such models, based on electrophysiological and behavioral data.

No abstract available.

Cancer is a heterogeneous disease, where multiple, phenotypically distinct subpopulations co-exist. Tumour evolution is a result of a complex interplay of genetic and epigenetic factors. Predicting the different cancer phenotypes requires linking the molecular state of a clone to its fate with high precision. Single-cell lineage tracing techniques are being successfully applied to the study of cancer evolution. I will show how single-cell assays endowed with genetic barcodes can be exploited to extract transcriptional stability properties of cancer clones, link molecular states to phenotypes, and study tumour heterogeneity, using a breast cancer model as a case study. Next, I will show how a new framework, called GALILEO, capturing lineage tracing, transcriptomic, and chromatin accessibility information simultaneously and at single-cell resolution, can be used to gain deep insights on aggressive cancer phenotypes. By tracing cancer cells in mice, we observe that the highly tumorigenic niche properly contains two transcriptionally distinct clonal subpopulations. Importantly, we identify a major epigenetic module common to them, which may explain the phenotypic relationship between these tumour-initiating subpopulations. In contrast, when exposed to cytotoxic treatment, the two cancer subpopulations show different drug sensitivity, both in vitro and in vivo. Finally, by tracing clonal response to chemotherapy across time, we identify two distinct paths to drug tolerance, each showing a gene expression bias that is reminiscent of the initial, untreated state. The possibility to associate the molecular features of cancer clones to their different outcomes, especially in the presence of epigenetic drivers, makes the proposed approach extremely valuable for the study of the evolution of cancer diseases at single-cell resolution.

I will give an overview of my recent, ongoing and future work in machine learning for biological sequences. This will cover in particular: – Microbial GWAS: tools to identify genetic determinants of phenotypic traits such as antimicrobial resistances. The importance of accessory genes in microbes make the usual focus on SNPs inappropriate. I am developing solutions based on k-mers, i.e., the presence of short sequences in the genomes. – Prediction from biological sequences: neural networks that take a biological sequence as input and predict a property of this sequence. This applies e.g. to regulatory genomics or fold prediction. I am working on regularization and on statistical inference on the features extracted by these networks. – Machine learning for evolutionary genomics: neural networks to infer parameters of sequence evolution models. For some complex models, likelihood maximization is too hard but sampling is easy. I use a large amount of simulated data to learn a function that reverses the model, and goes e.g. from a gene family to a phylogeny.

No environment is constant over time, and environmental fluctuations impact the outcome of evolutionary dynamics. Survival of a population not adapted to some environmental conditions is threatened unless a mutation rescues it, an eco-evolutionary process termed evolutionary rescue. I investigate evolutionary rescue in an environment that fluctuates between a favorable state, in which the population grows, and a harsh state, in which the population declines. I quantify the probability of evolutionary rescue using a stochastic framework with numerical and analytical tools, resulting in an exact computation of the population’s fate under deterministic versus stochastic environmental fluctuations. I also compare a perfectly harsh environment (i.e., fully birth-preventing) to an imperfectly harsh one (i.e., not fully birth-preventing) and identify which growth parameters promote evolutionary rescue using different population growth types. Because I find that the type of population growth is essential in assessing the fate of a population, I discuss the main current methods for inferring growth parameters from curve data. I identify their weaknesses, explain why fitting growth data from deterministic models can sometimes poorly estimate growth parameters, and finally attempt to develop an accurate inference method.

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