• The same genome, the same environment, but of distinct phenotype: Random chance can be a bliss for diversifying phenotypic portfolio. [False color SEM image of E. coli]
      • Synthetic biology: Can we recover the same level of precision and accuracy in biochemical information processing as in their silicon-based counterparts? [Source: http://www.ploscollections.org]
      • Metabolism as a network: What do you make of all this evolved plumbing? How is it different form plant engineering?
      • Population structure: Form not only follows but is followed by function.


      • GHIM Lab is seeking postdocs and PhD students from biology, physics, computer science or related training backgrounds who have strong motivation for interdisciplinary quantitative biology research. Do not hesitate to contact us.


      The Ghim Lab seeks to find the “design” principles behind the networks of biological information processing. The networks encompass cellular metabolism, regulation of gene expression, cell-to-cell communication, and up to social and ecological interactions among individuals. The current focus of research is on:

      ● Control and exploitation of noise in gene expression
      ● Resource allocation for coordinated metabolism
      ● Causal inference in neural information transfer
      ● Socio-economic dynamics in structured populations

      The aim from biology side is to rationalize the efficiency and robustness of biological networks in the light of form-function duality. The other side of the coin is an effort to restore the precision and accuracy of biochemical information processing in e.g. synthetic biology or metabolic engineering up to its silicon-based counterpart. Both of these objectives are closely linked to the fundamental problems of equilibrium and nonequilibrium statistical mechanics on complex networks. Information theory and statistical physics are a major tool toward this end but the crux is still the rigorous biological realism.