How can biological modeling help cell biology?

Cellular Logistics, together with a myriad of other journals, has been the proud publisher of definitive studies describing the molecular mechanisms governing intracellular processes, almost all uncovered by experimental approaches. Indeed, the very discipline of Cell Biology is built on empirical observations of cellular events at different scales, form the tissue to the atomic. In the vast majority of such studies, one or maximally few parameters (whatever they may be) are analyzed, and their behavior and/or relationship between them is described. And while some attempt is usually made in the Discussion of such studies to extend the particulars of such analyses to other molecules and/or processes, it remains impossible to integrate any particular analysis into the totality of cellular behavior. Classical Cell Biology simply does not have the ability to “put it all together” and unite all the molecular knowledge that we have amassed over the last decades to develop a comprehensive view of how cells function.

It seems that to move forward and evolve cell biology into comprehensive understanding will require new approaches and new tools. The available tools, essential for such integration of experimental data into a comprehensive picture of cellular networks, are encompassed by the field of Computational Cell Biology. Whether we like it or not, it is becoming clear that the next major scientific breakthroughs will occur at the level of combining experimental methods with mathematical modelling and simulations. Recently, different types of computational approaches have been used to develop models of various cellular processes including cell cycle, signaling circuits, membrane trafficking and cytoskeletal assembly, and move us towards a system-level understanding of such events.

The largest difficulty in modeling biological events lies in the vast complexity of biological systems. Cellular processes occur on multiple and drastically different spatio-temporal scales, from atomic interactions to organellar motility and beyond. Understanding how overall cellular behaviors arise for atomic/molecular reactions is extremely difficult, but improvements in modeling and computational model building tools may eventually simplify the behaviors of complex networks. The ultimate goal that seems to be particularly exciting is the possibility that once we understand the behaviors of single machines or organelles or even networks and the relationships between them, a new phenomenon may emerge that was unfathomable without the modeling and could not be obtained by experimentation alone since it's too vast and has too many parameters. Such large-scale behaviors must be understood to be able to develop predictive models of cellular function. And that predictive ability, the knowledge that if we tweak this, that will happen, is the Holy Grail of Cell Biology.

Modeling and computational approaches have provided fundamental new insight into many biological processes at vastly different scales, from organisms to tissues to cells to organelles to cellular components, all the way to atoms. They informed our understanding of the behaviors of separate organisms within a larger group such as flocking of pigeons,¹ collective migration of Dictyostellium² and bacterial biofilm formation,³ as well as organismal development and functionality such as tissue patterning in the zebrafish neural tube,⁴ the healing of fractured bone,⁵ or the opening of plant stomata in response to environmental stimuli to control plant transpiration (reviewed in ⁶). Computational modeling has been instrumental in understanding the processes governing the functionality of intracellular components such as mitochondrial bioenergetics,⁷ clathrin-mediated endocytosis,⁸ or filamentous actin dynamics.⁹ At a still more fundamental scale, mathematical approaches teach us about inter- and intra-molecular events, such as protein-to-protein channeling of cholesterol,¹⁰ metabolite fluxes,¹¹ or protein side-chain dynamics.¹²

While some cell biologists continue to reject the move towards the quantitative/predictive vision, some are ready to “drink the Cool-Aid” and try to embrace the novelty and the promise that computational approaches bring. I belong to the group who think that building the cross-over between experimental and computational analyses will bring us closer to obtaining system-level understanding of cellular life. To facilitate achieving this goal, we assembled three different views in this issue of Cellular Logistics: we showcase an effort to promote the interactions between classical cell biologists and modeling/computational scientists by targeted workshops (see the article entitled ”Finding your inner modeler: an NSF-sponsored workshop to introduce cell biologists to modeling/computational approaches”); include a philosophical musings on integrative biological simulations (see the article entitled “Integrative biological simulation praxis: considerations from physics, philosophy, and data/model curation practices”); and present an insightful description of agent-based approach to modeling in cell biology (see the article entitled “Agents and networks to model the dynamic interactions of intracellular transport”). We hope that the future will bring an increased awareness to how computational/modeling approaches can enhance cell biology experimentation.