One of the key challenges in community metabolic modeling is selecting an appropriate objective function – whether to maximize community growth or individual species growth. While community-level optimization enables predicting cooperative behaviors such as division of labor and cross-feeding, such microbial altruism lacks biological justification. Conversely, models maximizing individual growth often fail to reproduce experimentally observed interactions. This issue can be addressed by cybernetic modeling approach (Ramkrishna and Song, 2012; 2018). Cybernetic modeling views microorganisms as dynamic control systems regulating metabolism to maximize return-on-investment (ROI), yielding accurate predictions of complex microbial growth patterns on multiple substrates. While originally heuristic, this framework was later formalized by Young and Ramkrishna (2007) using optimal control theory. Their generalized control laws propose that microorganisms optimize metabolic regulation not for immediate gain, but to maximize ROI over a finite time horizon. Building on this formulation, we developed a species-resolved, dynamic cybernetic model for simulating microbial community interactions. We demonstrate our approach through a case study simulating a biopolymer-degrading microbial community, in which member species exhibit both cooperative and competitive behaviors depending on environmental context. Numerical simulations with our model revealed several emergent ecological dynamics that are challenging to capture with conventional approaches, including (1) the dominance of the primary degrader over the secondary degrader, (2) dynamic transitions in downstream interactions involving both competition and cross-feeding, and (3) overall community dynamics governed by upstream biopolymer degradation. This work presents a robust and versatile modeling framework for accurately simulating complex microbial interactions, offering a valuable in silico tool for advancing the understanding and engineering of both natural and synthetic microbial ecosystems.
References:
Ramkrishna, D., & Song, H.-S. (2012). Dynamic models of metabolism: Review of the cybernetic approach. AIChE Journal, 58(4), 986-997.
Ramkrishna, D., & Song, H.-S. (2018). Cybernetic modeling for bioreaction engineering. Cambridge University Press.
Young, J. D., & Ramkrishna, D. (2007). On the matching and proportional laws of cybernetic models. Biotechnology Progress, 23(1), 83-99.
