Building Subtraction Operators and Controllers via Molecular Sequestration

Samaniego, Christian Cuba; Qian, Yili; Carleton, Katelyn; Franco, Elisa

doi:10.1109/lcsys.2023.3294690

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Thus, the sequestration relationship between S 1 and A 1 resembles a simple perceptron with an AReLU activation function and weights of w 1 and − w 2 for inputs x 1 and x 2 , respectively. The linear relationship regime between and x 1 indicates that the steady-state available amount of S 1 is simply the difference between the total steady-state amounts of S 1 and A 1 , similar to other sequestration-based calculators demonstrated previously [42]. In other words, the outcome of the sequestration chemical reaction simply calculates the subtraction of total S 1 and A 1 and does not produce any product if S 1 is lower than A 1 .…”

Section: Resultssupporting

confidence: 73%

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: Resultssupporting

confidence: 73%

Engineering sequestration-based biomolecular classifiers with shared resources

Moghimianavval,

Gispert,

Castillo

et al. 2024

Preprint

Self Cite

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“…To analyze the transient dynamics in the regime of fast sequestration ( γ → ∞), we adopt the change of variables w 1 = x + c and w 2 = y + c , and rewrite the model as Following a similar analysis as in previous work [17], [18], we obtain the approximations c ≈ min( w 1 , w 2 ) and . These approximations are evident in computational simulations, as shown in Fig.…”

Section: Pulse Generator Dynamics: Stability and Exponential Approxim...mentioning

confidence: 84%

“…Sequestration occurs when two chemical species A and B interact via a second order reaction . If the sequestration reaction rate parameter γ is sufficiently large, and if timescale separation arguments can be applied, then this reaction can be used to compute the difference between the concentration of A and of B [17]. Here we take advantage of fast sequestration and its capacity to subtract signals to build a pulse generator circuit, whose topology is comparable to the topology of an IFFL [19].…”

Section: Introductionmentioning

confidence: 99%

Design of a sequestration-based network with tunable pulsing dynamics

Nakamura,

Cuba Samaniego,

Blanchini

et al. 2024

Preprint

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Incoherent feedforward networks exhibit the ability to generate temporal pulse behavior. However, exerting control over specific dynamic properties, such as amplitude and rise time, poses a challenge and is intricately tied to the network's implementation. In this study, we focus on analyzing sequestration-based networks capable of exhibiting pulse behavior. By employing time-scale separation in the fast sequestration regime, we approximate the temporal dynamics of these networks. This approach allows us to establish a mapping that elucidates the impact of varying the kinetic rates and pulse specifications, including amplitude and rise time. Furthermore, we introduce a positive feedback mechanism to regulate the amplitude of the pulsing response.

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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.

show abstract

Building Subtraction Operators and Controllers via Molecular Sequestration

Cited by 4 publications

References 25 publications

Engineering sequestration-based biomolecular classifiers with shared resources

Engineering sequestration-based biomolecular classifiers with shared resources

Design of a sequestration-based network with tunable pulsing dynamics

Engineering Sequestration-Based Biomolecular Classifiers with Shared Resources

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