More Like this

1. Engineering sequestration-based biomolecular classifiers with shared resources

   https://doi.org/10.1101/2024.04.15.589451  

   Moghimianavval, Hossein; Gispert, Ignacio; Castillo, Santiago R; Corning, Olaf_B_W H; Liu, Allen P; Samaniego, Christian Cuba (April 2024, bioRxiv)

   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 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 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. 

   more » « less
2. Principled neuromorphic reservoir computing

   https://doi.org/10.1038/s41467-025-55832-y  

   Kleyko, Denis; Kymn, Christopher J; Thomas, Anthony; Olhausen, Bruno A; Sommer, Friedrich T; Frady, E Paxon (January 2025, Nature communications)

   Reservoir computing advances the intriguing idea that a nonlinear recurrent neural circuit—the reservoir—can encode spatio-temporal input signals to enable efficient ways to perform tasks like classification or regression. However, recently the idea of a monolithic reservoir network that simultaneously buffers input signals and expands them into nonlinear features has been challenged. A representation scheme in which memory buffer and expansion into higher-order polynomial features can be configured separately has been shown to significantly outperform traditional reservoir computing in prediction of multivariate time-series. Here we propose a configurable neuromorphic representation scheme that provides competitive performance on prediction, but with significantly better scaling properties than directly materializing higher-order features as in prior work. Our approach combines the use of randomized representations from traditional reservoir computing with mathematical principles for approximating polynomial kernels via such representations. While the memory buffer can be realized with standard reservoir networks, computing higher-order features requires networks of ‘Sigma-Pi’ neurons, i.e., neurons that enable both summation as well as multiplication of inputs. Finally, we provide an implementation of the memory buffer and Sigma-Pi networks on Loihi 2, an existing neuromorphic hardware platform. 

   more » « less
3. Polymorphic Accelerators for Deep Neural Networks

   https://doi.org/10.1109/TC.2020.3048624  

   Azizimazreah, Arash; Chen, Lizhong (January 2021, IEEE Transactions on Computers)

   Deep neural networks (DNNs) come with many forms, such as convolutional neural networks, multilayer perceptron and recurrent neural networks, to meet diverse needs of machine learning applications. However, existing DNN accelerator designs, when used to execute multiple neural networks, suffer from underutilization of processing elements, heavy feature map traffic, and large area overhead. In this paper, we propose a novel approach, Polymorphic Accelerators, to address the flexibility issue fundamentally. We introduce the abstraction of logical accelerators to decouple the fixed mapping with physical resources. Three procedures are proposed that work collaboratively to reconfigure the accelerator for the current network that is being executed and to enable cross-layer data reuse among logical accelerators. Evaluation results show that the proposed approach achieves significant improvement in data reuse, inference latency and performance, e.g., 1.52x and 1.63x increase in throughput compared with state-of-the-art flexible dataflow approach and resource partitioning approach, respectively. This demonstrates the effectiveness and promise of polymorphic accelerator architecture. 

   more » « less
4. Signaling-based neural networks for cellular computation

   https://doi.org/10.23919/ACC50511.2021.9482800  

   Samaniego, Christian Cuba; Moorman, Andrew; Giordano, Giulia; Franco, Elisa (July 2021, 2021 American Control Conference (ACC))

   Cellular signaling pathways are responsible for decision making that sustains life. Most signaling pathways include post-translational modification cycles, that process multiple inputs and are tightly interconnected. Here we consider a model for phosphorylation/dephosphorylation cycles, and we show that under some assumptions they can operate as molecular neurons or perceptrons, that generate sigmoidal-like activation functions by processing sums of inputs with positive and negative weights. We carry out a steady-state and structural stability analysis for single molecular perceptrons as well as for feedforward interconnections, concluding that interconnected phosphorylation/dephosphorylation cycles may work as multilayer biomolecular neural networks (BNNs) with the capacity to perform a variety of computations. As an application, we design signaling networks that behave as linear and non-linear classifiers. 

   more » « less
5. Computer Simulations Show That Liquid–Liquid Phase Separation Enhances Self-Assembly

   https://doi.org/10.1021/acsnano.5c08120  

   Frechette, Layne B; Sundararajan, Naren; Caballero, Fernando; Trubiano, Anthony; Hagan, Michael F (August 2025, ACS Nano)

   Biomolecular condensates are liquid- or gel-like droplets of proteins and nucleic acids formed at least in part through liquid–liquid phase separation. Condensates enable diverse functions of cells and the pathogens that infect them, including self-assembly reactions. For example, it has been shown that many viruses form condensates within their host cells to compartmentalize capsid assembly and packaging of the viral genome. Yet, the physical principles controlling condensate-mediated self-assembly remain incompletely understood. In this article, we use coarse-grained molecular dynamics simulations to study the effect of a condensate on the assembly of icosahedral capsids. The capsid subunits are represented by simple shape-based models to enable simulating a wide range of length and time scales, while the condensate is modeled implicitly to study the effects of phase separation independent of the molecular details of biomolecular condensates. Our results show that condensates can significantly enhance assembly rates, yields, and robustness to parameter variations, consistent with previous theoretical predictions. However, extending beyond those predictions, the computational models also show that excluded volume enables control over the number of capsids that assemble within condensates. Moreover, long-lived aberrant off-pathway assembly intermediates can suppress yields within condensates. In addition to elucidating condensate-mediated assembly of viruses and other biological structures, these results may guide the use of condensates as a generic route to enhance and control self-assembly in human-engineered systems. 

   more » « less