The objectives for the fi rst two years will cover: (1) modelling and understanding the system biology of species communities, and (2) modelling and understanding the co-evolutionary aspects present in such communities.
Objective 1: Modelling and understanding the system biology of communities
Objective 1 will be divided into two objectives, one concerned with synthetic communities, and the other with natural ones. The reasons for this are double. The first is that synthetic communities are a good initial approximation of natural communities both in terms of the complexity of models that need to be developed and then algorithmically explored, and in terms of the experiments that may be performed a posteriori to validate the insights that the computational methods may provide. The second reason is that system biology of synthetic communities represents an area of research that the two teams have already in common.
Objective 1a: Synthetic communities.
The objective in this case is to explore how communities of microbes might be selected to efficiently synthetise compounds of interest in a much more rapid and e ffective way than is possible by natural means. This is done at the system level, considering mainly metabolism but also possibly gene regulation information. A first approach to this problem was explored in (A. Julien-Laferriere, et al., Scienti fic Reports, 6:29182, 2016) where a method is presented to identify a consortium of organisms that is best for the production of compounds of interest. In this work, only topological aspects of the metabolic networks of the organisms were considered. A natural extension to this work, that involves the inclusion of stoichiometry, will lead to a more complex problem but also provide more realistic solutions.
Objective 1b: Natural communities (in health).
The second context examines, at the molecular level, small communities of microbes living inside a host and how they interact among them and with the host. The main question explored here is what makes some members from these communities pathogenic while others are not, that is, what gives origin to the pathogenicity of some species. We explored this initially within the respiratory tract of swines and were able to predict and validate metabolic traits in the pathogenic species that were not present in the non-pathogenic ones (M. G. Ferrarini, et al., in revision).
Objective 2: Co-evolution of hosts and symbionts.
Cophylogeny allows to reconstruct the coevolutionary history of groups of species that interact over a long period of time and in ecologically close environments based on their phylogenetic information. This is important to, for example, understand how a pathogenic symbiont that is evolutionary associated to another species (its “host”) came from and the dynamics of such association, thus possibly enabling to design ways to control pathogenicity.
The focus in this case will be on three di fferent directions. The fi rst is to explore machine learning techniques to obtain better models of cophylogeny that take into account the presence of many errors and uncertainties in the input. The second is to improve the quality of the cophylogeny solutions obtained by filtering among the often huge number of equally good ones using ecological information. Finally, the third direction will be to de ne a mathematical model and a single unifi ed statistical framework that includes cophylogeny information into ecological network studies. Although it is commonly accepted that co-evolution acts on the interactions among species in ways that shape the ecological communities (J. N. Thompson, The Geographic Mosaic of Coevolution, University Of Chicago Press, 2005), none of the existing methods considers the cophylogeny of the species of interests. This would allow to better understand the dynamics of the past interactions between species and help predict future ones.
In the third year, a third objective will be addressed that represents the ultimate aim of COMPASSO.
Objective 3: Establishing links between infectious and non infectious diseases.
The objective for the last year (2020) will be to propose initial ways of addressing the ultimate aim of this proposal, namely to build the links between species interactions and cancer/rare diseases, or more precisely, between infectious and non infectious diseases.
