Multi-level and hybrid modelling approaches for systems biology

Abstract

During the last decades, high-throughput techniques allowed for the extraction of a huge amount of data from biological systems, unveiling more of their underling complexity. Biological systems encompass a wide range of space and time scales, functioning according to flexible hierarchies of mechanisms making an intertwined and dynamic interplay of regulations. This becomes particularly evident in processes such as ontogenesis, where regulative assets change according to process context and timing, making structural phenotype and architectural complexities emerge from a single cell, through local interactions. The information collected from biological systems are naturally organized according to the functional levels composing the system itself. In systems biology, biological information often comes from overlapping but different scientific domains, each one having its own way of representing phenomena under study. That is, the different parts of the system to be modelled may be described with different formalisms. For a model to have improved accuracy and capability for making a good knowledge base, it is good to comprise different system levels, suitably handling the relative formalisms. Models which are both multi-level and hybrid satisfy both these requirements, making a very useful tool in computational systems biology. This paper reviews some of the main contributions in this field.

Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: A software engineering perspective

“…References: In their 2017 review Bardini et al [2] argue that multiscale models in systems biology in general should strive towards a hybrid approach. The same has also previously been stated by other researchers [46][47][48].…”

Omics-Based Interaction Framework – a systems model to reveal molecular drivers of synergy

“…To formally test whether the effect of dual factors (F AB : Pam2-ODN) is greater than the expected linear sum of its individual factors (F A : Pam2; F B : ODN), an initial 2-level 2-factor (2 2 ) factorial design is required (Slinker, 1998; Foucquier et al , 2015) (Figure 1D). Our strategy adapts the traditional analysis of variance (ANOVA) approach into a model that links the empirical analysis of synergy (Slinker, 1998; Foucquier et al , 2015) with the high-throughput capacity and high-dimensionality of Omics datasets (Coral et al , 2017; Bardini et al , 2017). As summarized in Figure 1E, OBIF integrates statistical and biological interactions in Omics data matrices from single vs. dual factor exposures, leveraging Omics screening to promote discovery of the molecular drivers of synergy, and facilitating the biological validation of synergy regulators.…”

Modeling antibiotic resistance in the microbiota using multi-level Petri Nets

“…In particular, the capability of managing colored tokens, coupled with the hierarchical organization makes it easy to handle in a consistent way different information structures in the same model. This is a valuable tool when building complex biological models since different organizational levels of the model may have different ways of extracting data from experiments and information form data (interested readers may refer to [ 21 ] for a thorough analysis of these aspects).…”

“…The proposed model is based on Nets-Within-Nets (NWN), a high-level Petri Nets formalism supporting the development of multi-level and hybrid models suitable for stochastic and timed dynamic simulations [ 20 , 21 ].…”