Notions of similarity for computational biology models

Abstract

Computational models used in biology are rapidly increasing in complexity, size, and numbers. To build such large models, researchers need to rely on software tools for model retrieval, model combination, and version control. These tools need to be able to quantify the differences and similarities between computational models.However, depending on the specific application, the notion of "similarity" may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here, we introduce a general notion of quantitative model similarities, survey the use of existing model comparison methods in model building and management, and discuss potential applications of model comparison. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on different model aspects. Potentially relevant aspects of a model comprise its references to biological entities, network structure, mathematical equations and parameters, and dynamic behaviour. Future similarity measures could combine these model aspects in flexible, problem-specific ways in order to mimic users' intuition about model similarity, and to support complex model searches in databases."Over the past few decades, mathematical models of molecular and gene networks have become an important part of the research toolkit for the biosciences" [1]. Mathematical models are formal representations of natural systems that can help answer questions about the complex system they represent [2]. According to Robert Rosen, a model establishes a modelling relation between a formal and a natural system: the formal system encodes the natural system, and inferences made in the formal system can be interpreted (decoded ) as statements about the natural system [3]. Computational models in biology serve as abstractions of biological systems. Biochemical models, for example, associate model components, such as mathematical expressions, objects, or variables, with biochemical entities such as molecule species or chemical reactions. Depending on the scientific questions addressed and on the available data, a biological system may be described by models of different scopes and levels of granularity, reflecting different views of the system.Computational models can be based on a number of mathematical formalisms [1]. Here, again, the choice of a particular approach largely depends upon the type of question asked and on the data available [2]. Metabolic and signaling pathways are usually modelled by ordinary differential equation systems (ODEs), and the resulting models are known as kinetic models. Larger metabolic systems are typically described by constraint-based network models that capture stationary metabolic fluxes, but disregard enzyme kinetics. Gene expression dynamics can be modelled by kinetic models, stochastic processes, or discrete dynamic processes such as Boolean networks [4]. Spatial cell models may even involve partial differential equations (PDEs). In addition, the ris...

Linguistic Analysis of the bioRxiv Preprint Landscape

“…Examining the top five and bottom five preprints within the systems biology field reinforces PC1 ‘s dichotomous theme (Table 2). Preprints with the highest values [64,65,66,67,68] included software packages, machine learning analyses, and other computational biology manuscripts, while preprints with the lowest values [69,70,71,72,73] were focused on cellular signaling and protein activity. We provide the rest of our 50 generated PCs in our online repository (see Software and Data Availability).…”