Principles of Computation by Competitive Protein Dimerization Networks

Parres-Gold, Jacob; Levine, Matthew; Emert, Benjamin; Stuart, Andrew; Elowitz, Michael B.

doi:10.1101/2023.10.30.564854

Cited by 5 publications

(2 citation statements)

References 132 publications

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“…While a single sigma-based perceptron generates a tunable decision boundary even with sharing limited resources (Fig. 3), most biologically relevant biocomputation processes such as competitive ligand binding and protein dimerization generate sophisticated non-linear responses that rely on the protein-protein interactions and the concentration of competing dimerizing proteins or ligands [18,17]. Therefore, we aim to utilize the sigma-based perceptron as a basic building block of more intricate networks made of multiple perceptrons that are capable of generating non-linear outputs.…”

Section: Biomolecular Neural Network Generate Non-linear Classifiers ...mentioning

confidence: 99%

“…Competitive binding of only a few promiscuous ligands to various receptors has been demonstrated to accommodate a wide range of signaling activities in multicellular organisms [17]. Similarly, it was recently shown that competitive protein dimerization allows small protein monomer networks to encode an extensive range of homo-or hetero-dimeric outputs through precise adjustments in the concentration of network monomers [18]. In addition, competing transcription factors that bind to the same RNA polymerase may affect the output of engineered genetic circuits in bacteria.…”

Section: Introductionmentioning

confidence: 99%

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Engineering sequestration-based biomolecular classifiers with shared resources

Moghimianavval,

Gispert,

Castillo

et al. 2024

Preprint

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Constructing molecular classifiers that enable cells to recognize linear and non-linear input patterns would expand the biocomputational capabilities of engineered cells, thereby unlocking their potential in diagnostics and therapeutic applications. While several biomolecular classifier schemes have been designed, the effect of biological constraints such as resource limitation and competitive binding on the function of those classifiers has been left unexplored. Here, we first demonstrate the design of a sigma factor-based perceptron as a molecular classifier working on the principles of molecular sequestration between the sigma factor and its anti-sigma molecule. We then investigate how the output of the biomolecular perception, i.e., its response pattern or decision boundary, is affected by the competitive binding of sigma factors to a pool of shared and limited resources of core RNA polymerase. Finally, we reveal the influence of sharing limited resources on multi-layer perceptron neural networks and outline design principles that enable the construction of non-linear classifiers using sigma-based biomolecular neural networks in the presence of competitive resource-sharing effects.

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Section: Biomolecular Neural Network Generate Non-linear Classifiers ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Engineering sequestration-based biomolecular classifiers with shared resources

Moghimianavval,

Gispert,

Castillo

et al. 2024

Preprint

View full text Add to dashboard Cite

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Engineering Sequestration-Based Biomolecular Classifiers with Shared Resources

Moghimianavval,

Gispert,

Castillo

et al. 2024

ACS Synth. Biol.

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Constructing molecular classifiers that enable cells to recognize linear and nonlinear input patterns would expand the biocomputational capabilities of engineered cells, thereby unlocking their potential in diagnostics and therapeutic applications. While several biomolecular classifier schemes have been designed, the effects of biological constraints such as resource limitation and competitive binding on the function of those classifiers have been left unexplored. Here, we first demonstrate the design of a sigma factor-based perceptron as a molecular classifier working based on the principles of molecular sequestration between the sigma factor and its antisigma molecule. We then investigate how the output of the biomolecular perceptron, i.e., its response pattern or decision boundary, is affected by the competitive binding of sigma factors to a pool of shared and limited resources of core RNA polymerase. Finally, we reveal the influence of sharing limited resources on multilayer perceptron neural networks and outline design principles that enable the construction of nonlinear classifiers using sigma-based biomolecular neural networks in the presence of competitive resource-sharing effects.

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Single Cells Signal Non-self from Self via ‘XOR’ and ‘NOT EQUALS’ Logic in a Dimer of Dimers

An,

Prakash

2024

Preprint

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The ciliate Tetrahymena thermophila's seven mating types are defined by unique receptor-ligand pairs (Mta/Mtb). While Mta and Mtb are known to participate in a mating signal complex, how they distinguish between oneself and six non-self cell types remains unknown. AlphaFold3 predictions reveal Mta/Mtb as large glycoproteins likely derived from ancient, unisexual, intercellular adhesion molecules. Since homologous binary-type systems perform XOR by switching mtA expression, we show spectrum (n) types naturally extend XOR to multi-bit NOT EQUALS operations via differential affinities of Mta/Mtb dimers. We model kinetics begetting the 'n + 1th' type, demonstrating self-inhibition by trans-homophilic Mtb-Mtb. A computational approach reconciles recent and classical evidence for mating exclusivity, including selfing failures (same type mating). Binding kinetics enables fast, robust intercellular computation across an intermembrane mating space. Thus, Mta/Mtb families are a model system allowing us to derive a 'calculus' of antigenic variation and inspire synthetic designs of XOR logic underlying self/non-self recognition.

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Principles of Computation by Competitive Protein Dimerization Networks

Cited by 5 publications

References 132 publications

Engineering sequestration-based biomolecular classifiers with shared resources

Engineering sequestration-based biomolecular classifiers with shared resources

Engineering Sequestration-Based Biomolecular Classifiers with Shared Resources

Single Cells Signal Non-self from Self via ‘XOR’ and ‘NOT EQUALS’ Logic in a Dimer of Dimers

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