Arcak, M. | Search bioRxiv

bioRxiv2024

Learning Biomolecular Models using Signal Temporal Logic

Modeling dynamical biological systems is key for understanding, predicting, and controlling complex biological behaviors. Traditional methods for identifying governing equations, such as ordinary differential equations (ODEs), typically require extensive quantitative data, which is often scarce in biological systems due to experimental limitations. To address this challenge, we introduce an approach that determines biomolecular models from qualitative system behaviors expressed as Signal Temporal Logic (STL) statements, which are naturally suited to translate expert knowledge into computationally tractable specifications. Our method represents the biological network as a graph, where edges represent interactions between species, and uses a genetic algorithm to identify the graph. To infer the parameters of the ODEs modeling the interactions, we propose a gradient-based algorithm. On a numerical example, we evaluate two loss functions using STL robustness and analyze different initialization techniques to improve the convergence of the approach.

bioengineering↗

bioRxiv2019

Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling

1Designing and implementing synthetic biological pattern formation remains a challenge due to underlying theoretical complexity as well as the difficulty of engineering multicellular networks bio-chemically. Here, we introduce a \"cell-in-the-loop\" approach where living cells interact through in silico signaling, establishing a new testbed to interrogate theoretical principles when internal cell dynamics are incorporated rather than modeled. We present a theory that offers an easy-to-use test to predict the emergence of contrasting patterns in gene expression among laterally inhibiting cells. Guided by the theory, we experimentally demonstrated spontaneous checkerboard patterning in an optogenetic setup where cell-to-cell signaling was emulated with light inputs calculated in silico from real-time gene expression measurements. The scheme successfully produced spontaneous, persistent checkerboard patterns for systems of sixteen patches, in quantitative agreement with theoretical predictions. Our research highlights how tools from dynamical systems theory may inform our understanding of patterning, and illustrates the potential of cell-in-the-loop for engineering synthetic multicellular systems.

synthetic biology↗