Models of signalling networks – what cell biologists can gain from them and give to them

Abstract

SummaryComputational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are crosstrained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally. Key words: Cell signalling, Computational biology, Systems biologyIntroduction A decade ago, we welcomed the first signalling-network models that were strongly grounded in wet-lab experiments (Hoffmann et al., 2002;Schoeberl et al., 2002). Excellent models now exist for many canonical signalling circuits in a variety of biological settings. However, such models should not be viewed as an end product but rather as a tool for addressing systems-level challenges in cell biology . Have models fulfilled this role and have they provided biological insights that experimentalists should bother to care about? Here, we answer 'Yes' to both questions and predict that signallingnetwork models will soon become indispensable for modern research in the field. Fortunately, the current wealth of dataintensive methods has primed today's cell biologists to embrace modelling, even though they may lack formal training in the underlying mathematics.In this Opinion, we propose that empirical cell biologists have much to gain from signalling-network models, and much to give by ensuring that these models stay in touch with reality. We begin with a brief primer on how computational models can be critically assessed from a biological standpoint. Then, we walk through a series of important insights about cell signalling that have stemmed from computational-systems modelling. We conclude with future perspectives on where signalling-network models are just beginning to have an impact and will continue to do so in the coming years.

Quantitative Analysis of Receptor Tyrosine Kinase-Effector Coupling at Functionally Relevant Stimulus Levels

“…The capture and detection antibodies for pERK do not discriminate between pERK1 and pERK2 (56), and those used for the pAkt ELISA detect both human and mouse pAkt1 (57). The standard proteins were reconstituted in lysate from unstimulated cells at concentrations of 0.1, 1,4,8,10,12,15,20,30, and 40 ng/ml, and the resulting signals were measured using the standard ELISA protocols described above. Lysate from 6 ϫ 10 5 cells stimulated with 4 nM ART for 10 min was included in the same experiments, and the concentration of pERK or pAkt present in these samples was read from the standard curve.…”

“…Our quantitative understanding of the activation and signaling mechanisms of cytokine and growth factor (GF) 4 receptors has advanced substantially in recent years (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). Yet because of the high complexity of the problem, and the often contextdependent behavior of many intracellular signaling events and processes, progress toward a quantitative understanding of how cells orchestrate their response to external stimuli remains in its infancy.…”

The Value of Mechanistic Biophysical Information for Systems-Level Understanding of Complex Biological Processes Such as Cytokinesis

“…Understanding the operation of complex biological systems will remain a challenge for the foreseeable future, so theoretical approaches ranging from abstract to molecularly explicit models are growing in importance in biology (1). At the abstract end of the spectrum genetic (2) and protein (3) interaction maps provide insights about the architecture of systems as well as strong constraints for models (1,4).…”

“…At the abstract end of the spectrum genetic (2) and protein (3) interaction maps provide insights about the architecture of systems as well as strong constraints for models (1,4). For example, genetic analysis provided enough information to model the budding yeast cell cycle with little information about the numbers of molecules or their reaction rates (5).…”

Insulin signaling dynamics in human adipocytes : Mathematical modeling reveals mechanisms of insulin resistance in type 2 diabetes

“…To add on to this complexity, different kinds of ligands, as well as ligand abundance, location, and dynamics give rise to different intracellular responses [8]. To handle all this complexity and dissect the information in experimental data, mathematical modeling is coming of age as an important tool for data analysis [9]. We use mathematical modeling as a tool to get maximal information out of our obtained experimental data and also to more intelligently design new experiments.…”