Mechanistic biological modeling thrives

In their Perspective “Systems biology (un)certainties” (23 October 2015, p. [386][1]), P. D. W. Kirk et al. may give readers the impression that mathematical modeling and computer simulations of biological systems have fallen on hard times, owing to difficulties in measuring parameter values. The authors write that quantitative modeling in biology is under attack for applying data-driven models whose structure and parameters lack physical meaning. They suggest that such abstract models are necessary because biological parameters are hard to measure. In spite of their intrinsic limitations ([ 1 ][2]), we agree that abstract models can be powerful tools to address systems such as gene networks. We also agree that uncertainties should be reported. However, we wish to stress that physically motivated, mechanistic modeling of complex biological systems is thriving, driven in large part by increasingly accurate measurements of molecular numbers, activities, and organization in live cells ([ 2 ][3]) and even in whole organisms ([ 3 ][4]). Whenever available, such information should be incorporated to constrain the structure and parameters, as it improves the model's ability to identify the mechanism. Thus, it is misleading to write that “fundamental physical laws…often do not provide a good starting point for understanding how biological organisms and systems work” or that “the abundances of all the key players (molecules, cells, or individuals) cannot be measured simultaneously and continuously.” Microscopically realistic modeling, constrained by independent parameter measurement, has driven the physical sciences for centuries. As in these fields, model predictions in modern quantitative biology often motivate new experiments that yield parameter values and structural revisions, and successful models converge on the actual molecular mechanism over time. Only mechanistic models allow physical principles and intuition gleaned from countless previous studies to be called upon to guide model development. 1. [↵][5]1. S. Brenner , Philos. Trans. R. Soc. London Ser. B Biol. Sci. 365, 207 (2010). [OpenUrl][6][Abstract/FREE Full Text][7] 2. [↵][8]1. D. Vavylonis, 2. J.-Q. Wu, 3. S. Hao, 4. B. O'Shaughnessy, 5. T. D. Pollard , Science 319, 97 (2008). [OpenUrl][9][Abstract/FREE Full Text][10] 3. [↵][11]1. F. B. Shipley, 2. C. M. Clark, 3. M. J. Alkema, 4. A. M. Leifer , Front. Neural Circuits 8, 28 (2014). [OpenUrl][12][PubMed][13] [1]: /lookup/doi/10.1126/science.aac9505 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #xref-ref-1-1 "View reference 1 in text" [6]: {openurl}?query=rft.jtitle%253DPhilosophical%2BTransactions%2Bof%2Bthe%2BRoyal%2BSociety%2BB%253A%2BBiological%2BSciences%26rft.stitle%253DPhil%2BTrans%2BR%2BSoc%2BB%26rft.aulast%253DBrenner%26rft.auinit1%253DS.%26rft.volume%253D365%26rft.issue%253D1537%26rft.spage%253D207%26rft.epage%253D212%26rft.atitle%253DSequences%2Band%2Bconsequences%26rft_id%253Dinfo%253Adoi%252F10.1098%252Frstb.2009.0221%26rft_id%253Dinfo%253Apmid%252F20008397%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [7]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Njoicm95cHRiIjtzOjU6InJlc2lkIjtzOjEyOiIzNjUvMTUzNy8yMDciO3M6NDoiYXRvbSI7czoyNDoiL3NjaS8zNTEvNjI3MC8yMzQuMy5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [8]: #xref-ref-2-1 "View reference 2 in text" [9]: {openurl}?query=rft.jtitle%253DScience%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.1151086%26rft_id%253Dinfo%253Apmid%252F18079366%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [10]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjExOiIzMTkvNTg1OS85NyI7czo0OiJhdG9tIjtzOjI0OiIvc2NpLzM1MS82MjcwLzIzNC4zLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [11]: #xref-ref-3-1 "View reference 3 in text" [12]: {openurl}?query=rft.jtitle%253DFront.%2BNeural%2BCircuits%26rft.volume%253D8%26rft.spage%253D28%26rft_id%253Dinfo%253Apmid%252F24715856%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [13]: /lookup/external-ref?access_num=24715856&link_type=MED&atom=%2Fsci%2F351%2F6270%2F234.3.atom