Why model?

Abstract

Next generation sequencing technologies are bringing about a renaissance of mining approaches. A comprehensive picture of the genetic landscape of an individual patient will be useful, for example, to identify groups of patients that do or do not respond to certain therapies. The high expectations may however not be satisfied if the number of patient groups with similar characteristics is going to be very large. I therefore doubt that mining sequence data will give us an understanding of why and when therapies work. For understanding the mechanisms underlying diseases, an alternative approach is to model small networks in quantitative mechanistic detail, to elucidate the role of gene and proteins in dynamically changing the functioning of cells. Here an obvious critique is that these models consider too few components, compared to what might be relevant for any particular cell function. I show here that mining approaches and dynamical systems theory are two ends of a spectrum of methodologies to choose from. Drawing upon personal experience in numerous interdisciplinary collaborations, I provide guidance on how to model by discussing the question “Why model?”

Problems, challenges and promises: perspectives on precision medicine

“…For a comparison of the relative strengths and weakness of each approach see 'Why model' by Wolkenhauer [34]. While such subdivision can be informative we should be cautious of fostering tribal rivalries by thinking of these approaches as mutually exclusive.…”

Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies

“…Data are often collected from disparate sources and organized into a coherent structure that can be interrogated by graph theory or logical (probabilistic) approaches 25. Additionally, they can be applied in a flexible manner to multi‐omics and clinical data, and across scales.…”

“…4). 25 The activity and interaction of autonomous “agents” (e.g., cells), consisting of simple behavioral rules, may be formulated to simulate the collective behavior of these agents. As yet, this is not an approach that has been applied to the study of osteoarthritis associated cells and tissues, but has found utility in other complex conditions 66…”

“…Decision‐making heuristics and learning processes may be applied to simulate the complex behavior of the system. Such approaches have been frequently applied to multi‐scale problems67 and may be applicable to modelling the complex behavior within, and between, tissues 25. (E) Highly detailed geometric information derived from advanced‐imaging techniques and material properties can be used in finite‐element models of multiple musculoskeletal tissues 62…”

“…In isolation the relative contributions of biology, structure, and mechanics may not result in OA, but rather an understanding of the interplay, and common regulatory mechanisms, between these components of joint health is required 21. It is likely that we need to consider therapeutic options that target multiple tissues to tackle OA, consequently, appropriate mechanistic modelling approaches to compare between cell types is required to establish therapeutic targets within signalling pathways that are relevant to both tissues 25. This is exemplified by the emerging discipline of systems pharmacology.…”