Biosys-LiDeOGraM: A visual analytics framework for interactive modelling of multiscale biosystems

Abstract

In this paper, we present a test of an interactive modelling scheme in real conditions. The aim is to use this scheme to identify the physiological responses of microorganisms at different scales in a real industrial application context. The originality of the proposed tool, Biosys-LiDeOGraM, is to generate through a human-machine cooperation a consistent and concise model from molecules to microbial population scales: If multi-omics measurements can be connected relatively easily to the response of the biological system at the molecular scale, connecting them to the macroscopic level of the biosystem remains a difficult task, where human knowledge plays a crucial role. The use-case considered here pertains to an engineering process of freeze-drying and storage of Lactic Acid Bacteria. Producing a satisfying model of this process is a challenge due to (i) the scarcity and variability of the experimental dataset, (ii) the complexity and multi-scale nature of biological phenomena, and (iii) the wide knowledge about the biological mechanisms involved in this process. The Biosys-LiDeOGraM tool has two main components that can have to be utilized in an iterative manner: the Genomic Interactive Clustering (GIC) module and the Interactive Multi-Scale modellIng Exploration (IMSIE) module, both involve users in their learning loops. Applying our approach to a dataset of 2,741 genes, an initial model, as a graph involving 33 variables and 165 equations, was first built. Then the system was able to interactively improve a synthetic version of this model using only 27 variables and 16 equations. The final graph providing a consistent and explainable biological model. This graphical representation allows various user interpretations at local and global scales, an easy confrontation with data, and an exploration of various assumptions. Finally Biosys-LiDeOGraM is easily transferable to other use-cases of multi-scale modelling using functional graphs. Author summaryThe use of "omics" data for understanding biological systems has become prevalent in several research domains. However, the data generated from diverse macroscopic scales used for this purpose is highly heterogeneous and challenging to integrate. Yet, it is crucial to incorporate this information to gain a comprehensive understanding of the underlying biological system. Although various integrative analysis methods that have been developed provide predictive molecular-scale models, they only offer a mechanistic view of the biological system at the cellular level. In addition, they often focus on specific biological hypotheses through dedicated case studies, making it difficult to apply their results to other scientific problems. To address these issues, we propose an interactive multi-scale modelling approach to integrate cross-scale relationships providing predictive and potentially explanatory models. A proof-of-concept tool has been developed and was validated in the context of the bioproduction of Lactococcus lactis, a bacterial species of high economic interest in the food industry and for which the control of the bioprocess is essential to guarantee its viability and functionality. Our approach can be applied to any biological system that can be defined through a set of variables, constraints and scales.