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Modeling and Design of Synthetic Biochemical Circuits for Biological Phenotypes


Bhamidipati, Pranav Subramanyam (2024) Modeling and Design of Synthetic Biochemical Circuits for Biological Phenotypes. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/gpc6-hb40.


Biological behaviors arise from the dynamical interactions of biochemical networks. For example, the various immune responses to damage are manifestations of signaling networks between immune cell types. A central goal in systems and synthetic biology is to elucidate the design principles of these networks, or circuits, both in the sense of dissecting how function arises from structure in the natural context and in the sense of understanding the guidelines for optimal engineering of synthetic biological systems. The study of design principles in both senses is aided by mathematical modeling and simulation, which provide a self-consistent framework for evaluating the theoretical implications of biological hypotheses as well as a testbed for the development of novel circuits for desired biological phenotypes. This thesis pertains to two related challenges in this field, namely the scaling of computational design to larger circuits and the engineering of global phenotypes that emerge nonlinearly from local interactions.

The first section of this thesis presents a novel design platform for biological circuits, called CircuiTree, that uses a game-playing paradigm to overcome the combinatorial complexity of \textit{de novo} circuit design. This platform treats circuit design as a game of circuit assembly and traverses the tree of possible assemblies using Monte Carlo tree search (MCTS). Borrowed from artificial intelligence (AI) agents that have mastered complex games, MCTS is a reinforcement learning (RL)-based search algorithm that efficiently searches for the most effective design strategies and naturally discovers design principles in the form of network motifs, which appear as clusters of solutions in the search tree. Finally, when tasked with designing fault-tolerant oscillators with five components, CircuiTree finds a novel design strategy, which we call motif multiplexing, in which multiple sub-oscillators are interleaved so as to render the circuit highly resistant to deletions and knockdowns. This design principle, which may be responsible for the multiple oscillatory loops observed in eukaryotic circadian clocks, opens the possibility of engineering synthetic circuits at a larger scale and suggests that larger biological circuits contain yet-unknown design features that are not simply extensions of smaller circuits.

The second section describes a novel mechanosensitive property of the SynNotch synthetic chimeric receptor and uses a multicellular modeling framework to show how it can be used to control spatiotemporal patterning \textit{in vitro}. Modified from the endogenous juxtacrine receptor Notch, SynNotch binds to an arbitrary extracellular ligand and, in response, releases an arbitrary transcription factor, thus acting as a user-defined signal transducer. We show that, in mouse fibroblasts, a simple sender-receiver SynNotch circuit ceases to transduce a membrane-bound GFP signal at high cell densities in 2D culture. Because of this feature, a lawn of cells expressing a signal-relay circuit, which we call the transceiver circuit, can undergo spatially limited activation, where the signal propagates in a wave outward from a GFP-expressing sender cell until, due to cell division, the cell density crosses a threshold value and the signaling system shuts down. Using a multicellular lattice-based model combined with experiments, we demonstrate that perturbations of growth parameters can be used to control the size of activated spots. Finally, we achieve spatiotemporal patterns of activation by seeding the growth dish nonuniformly, creating a wave of activation at the millimeter scale that recapitulates the kinematic wave patterning phenomenon observed during vertebrate somitogenesis.

Together, this body of work represents an advance in the use of computational methods and mathematical modeling to guide the design and control of complex biological phenotypes. Advances in these methods promise to catalyze the development of more advanced cell-based therapies and engineered tissues.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:synthetic biology, computational biology, artificial intelligence, reinforcement learning
Degree Grantor:California Institute of Technology
Division:Biology and Biological Engineering
Major Option:Bioengineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Thomson, Matthew
Thesis Committee:
  • Elowitz, Michael B. (chair)
  • Bois, Justin S.
  • Barr, Alan H.
  • Thomson, Matthew
Defense Date:19 March 2024
Funding AgencyGrant Number
Packard FoundationUNSPECIFIED
Heritage Medical Research Institute (HMRI)UNSPECIFIED
Record Number:CaltechTHESIS:06012024-054725051
Persistent URL:
Related URLs:
URLURL TypeDescription paper adapted for ch,. 3 DocumentarXiv paper: Cell density controls signal propagation waves in a multicellular synthetic gene circuit
Bhamidipati, Pranav Subramanyam0000-0002-6199-6505
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:16459
Deposited By: Pranav Bhamidipati
Deposited On:03 Jun 2024 23:19
Last Modified:17 Jun 2024 20:06

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