Computing a Worm: Reverse-Engineering Planarian Regeneration

  • Daniel Lobo
  • Michael Levin
Part of the Emergence, Complexity and Computation book series (ECC, volume 23)


In order to understand and control complex biological systems, we need to unravel the information processing and computations required to regulate their dynamics. The development of a complete organism from a single cell or the restoration of lost structures and body parts after amputations require the coordination of millions of cells exchanging and processing information. Understanding these dynamic processes from the results of biological perturbation experiments represent an outstanding challenge due to the characteristic non-linear dynamics and feed-back loops of their molecular and biophysical regulatory mechanisms—an inverse problem with no analytical or computationally tractable solutions. To bridge the gap between molecular-level mechanistic data and systems-level outcomes, we have developed a computational methodology based on heuristic algorithms to automatically reverse-engineer dynamic regulatory networks directly from experimental results. Using this method, applied to problems of pattern regulation during metazoan regeneration, we inferred the first comprehensive regulatory network of planarian regeneration, capable of explaining the most relevant experiments of anterior-posterior specification during regeneration. Here we summarize our results and study the dynamics of the inferred regulatory model, unraveling the information processing and computations required to regenerate a correct morphology.


Candidate Model Master Node Slave Node Morphological Outcome Formal Ontology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by NSF grant EF-1124651, NIH grant GM078484, USAMRMC grant W81XWH-10-2-0058, and the Mathers Foundation. Computation used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by NSF grant ACI-1053575, and a cluster computer awarded by Silicon Mechanics.


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© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of Biological SciencesUniversity of Maryland, Baltimore CountyBaltimoreUSA
  2. 2.Department of BiologyCenter for Regenerative and Developmental Biology, Tufts UniversityMedfordUSA

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