Section: Overall Objectives

Overall Objectives

The expanded name for the Beagle research group is “Artificial Evolution and Computational Biology”. Our aim is to position our research at the interface between biology and computer science and to contribute new results in biology by modeling biological systems. In other words we are making artifacts – from the Latin artis factum (an entity made by human art rather than by Nature) – and we explore them in order to understand Nature. The team is an Inria Project-Team since January, 2014. It gathers researchers from Inria, INSA, UCBL, who are members of three different labs, the LIRIS (Laboratoire d'Informatique en Image et Systèmes d'Information: UMR 5205 CNRS, INSA-Lyon, Univ. Claude Bernard Lyon 1, Univ. Louis Lumière Lyon 2, École Centrale de Lyon), the LBBE (Laboratoire de Biometrie et Biologie Evolutive: UMR CNRS 5558, Univ. Claude Bernard Lyon 1.), and CARMEN (Laboratoire de Recherche en Cardiovasculaire, Métabolisme, Diabétologie et Nutrition: UMR U1060 INSERM, INSA-Lyon, INRA 1235, Univ. Claude Bernard Lyon 1.). It is led by Prof. Guillaume Beslon (INSA-Lyon, LIRIS, Computer Science Dept.).

Our research program requires the team members to have skills in computer science but also in life sciences: they must have or develop a strong knowledge in biosciences to interact efficiently with biologists or, ideally, to directly interpret the results given by the models they develop. A direct consequence of this claim is that it is mandatory to restrict the domain of expertise in life sciences. This is why we focus on a specific scale, central in biology: the cellular scale. Indeed, we restrict our investigations on the cell, viewed as a dynamical system made of molecular elements. This specific scale is rich in open questions that deserve modeling and simulation approaches. We also focus on two different kinds of constraints that structure the cellular level: biophysical constraints and historical constraints. The cell is a system composed of molecules that physically interact and the spatio-temporal nature of these interactions is likely to strongly influence its dynamics. But the cell is also the result of an evolutionary process that imposes its own limits on what can evolve (or is the most likely to evolve) and what cannot (or is the less likely to evolve). A better understanding of what kind of systems evolution is the most likely to lead to in a given context could give us important clues for the analysis of extant biological systems.

To study these two kinds of constraints we mainly rely on two specific tools: computational cellular biochemistry and evolution models. We use these tools to develop our “artifacts” and we compare their output with real data, either direct measurements collected by experimentalists or ancestral properties computationally inferred from their extant descendants. Hence, the team research is organized in four main research axes. The first two ones are methodologically-oriented: we develop general formalisms and tools for computational cellular biochemistry (research axis 1) and families of models to study the evolutionary process (research axis 2). The last two ones are more biologically oriented: they focus on specific questions in cell biology and evolutionary biology that mobilize a general effort of the team. These two axes are the “NeuroCell” axis (research axis 3), in which biochemical models are specifically applied on brain cells (neurons and glia), and the “EvoEvo” axis (research axis 4) in which we tackle the question of the evolution of the evolutionary process in microorganisms. The next four sections describe these four axes in more details. Evidently the two specific biological questions that constitute axes 3 and 4 are not the sole topics tackled by the team. They are the ones that mobilize a substantial fraction of the researchers on the long run. Many other questions are tackled by individual researchers or even small groups. In the following these ones will be briefly described in their methodological context, i.e. in the two sections devoted to research axes 1 and 2.

Research axis 1: Computational cellular biochemistry

Biochemical kinetics developed as an extension of chemical kinetics in the early 20th century and inherited the main hypotheses underlying Van't Hoff's law of mass action: a perfectly-stirred homogeneous medium with deterministic kinetics. This classical view is however challenged by recent experimental results where the diffusive motion of many proteins in cellular media exhibits deviations from the ideal case of Brownian motion, in the form of position-dependent diffusion or anomalous diffusion, a hallmark of poorly mixing media. In this modern-day framework, cellular media appear as heterogeneous collections of contiguous spatial domains with different characteristics, thus providing spatial organization of the reactants. Moreover, the number of implicated reactants is often small enough that stochasticity cannot be ignored. To improve our understanding of intracellular biochemistry, we study spatiotemporal biochemical kinetics using computer simulations (particle-based spatially explicit stochastic simulations) and mathematical models (age-structured PDEs).

Research axis 2: Models for Molecular Evolution

We study the processes of genome evolution, with a focus on large-scale genomic events (rearrangements, duplications, transfers). We are interested in uncovering general laws which explain the organization of the genomes we observe today, and leveraging this knowledge to reconstruct some aspects of the history of life. To do so, we construct computational and mathematical models and apply them either in a “forward” way, i.e. observing the course of evolution from known ancestors and parameters, either by simulation (in silico experimental evolution) or by mathematical analysis (theoretical biology), or in a “backward” way, i.e. reconstructing ancestral states and parameters from known extant states (phylogeny, comparative genomics). Moreover we often mix the two approaches either by validating backwards reconstruction methods on forward simulations, or by using the forward method to test evolutionary hypotheses on biological data.biodiversity.

Research axis 3: Computational systems biology of neurons and astrocytes

Brain cells are rarely considered by computational systems biologists, though they are especially well suited for the field: their major signaling pathways are well characterized, the cellular properties they support in vivo are well identified (e.g. synaptic plasticity) and eventually give rise to well known functions at the organ scale (learning, memory). Moreover, electro-physiology measurements provide us with an experimental monitoring of signaling at the single cell level (sometimes at the sub-cellular scale) with unrivaled temporal resolution (milliseconds) over durations up to an hour. In this research axis, we develop modeling approaches for systems biology of both neuronal cells and glial cells, in particular astrocytes. We are mostly interested in understanding how the pathways implicated in the signaling between neurons, astrocytes and neurons-astrocytes interactions implement and regulate synaptic plasticity. We also tackle the more functional question of neural encoding of acoustic perception.

Research axis 4: EvoEvo

Variation and Selection are the two core processes of Darwinian Evolution. Yet, both are directly regulated by many processes that are themselves products of evolution (e.g. DNA repair, mutator genes, transposable elements, horizontal transfer, stochasticity of gene expression, sex, network modularity, niche construction...). This results in the ability of evolution to self-modify its operators, hence its dynamics. This process has been called "Evolution of Evolution" or EvoEvo [59]. Different EvoEvo strategies have been proposed in the literature, including regulation of variability, robustness/evolvability strategies, bet-hedging... However, most of these strategies are poorly characterized and the conditions under which they evolve as well as their consequences are generally unknown. On the current evaluation period, Beagle has taken the lead of the European Project “EvoEvo” that specifically focused on Evolution of Evolution by studying its biological mechanisms, evolutionary consequences and possible applications to bioinspired computation (evolutionary-base subspace clustering). Indeed, the EvoEvo project has had a profound structuring effect on the team since it involved more than half of it and led to the hiring of two PhD students and two engineers during the evaluation period.

The scientific objective of the Beagle team is to develop a consistent set of concepts and tools – mainly based on computational science – to in fine contribute to knowledge discovery in systems biology. Our strategy is to develop strong interactions with life science researchers to become active partners of the biological discovery process. Thus, our aim as a team is not to be a computer science team interacting with biologists, nor to be a team of biologists using computer science tools, but rather to stay in the middle and to become a trading zone [56] between biology and computer science. Our very scientific identity is thus fuzzy, melting components from both sciences. Indeed, one of the central claims of the team is that interdisciplinarity involves permanent exchanges between the disciplines. Such exchanges can hardly be maintained between distant teams. That's why the Beagle team tries to develop local collaborations with local scientists. That's also why Beagle also tries to organize itself as an intrinsically interdisciplinary group, gathering different sensitivities between biology and computer science inside the group. Our ultimate objective is to develop interdisciplinarity at the individual level, all members of the team being able to interact efficiently with specialists from both fields.