Advances in deep neural networks (DNNs) have revolutionized our ability to model complex data via the capacity to approximate continuous functions with arbitrary accuracy. DNN models have been used to achieve or surpass human-level performance in tasks such as image classification, generative modeling, and scientific discovery. Recent studies of chemical reaction networks (CRNs) suggest...
Synthetic chemical reaction networks (CRNs) typically operate in closed systems with finite reactants, limiting most circuits to a single round of computation. This constraint prevents sustained dynamics such as repeated execution of Boolean logic.
Here a simple motif, called a CRN buffer, is described that enables repeated circuit operation. CRN buffers consist of high concentration...
We investigate a reservoir computing framework based on unimolecular (linear) chemical reaction networks. While the mean-field dynamics are linear, stochastic dynamics can induce richer input–output behaviour through long-time statistics. Using tools from ergodic theory and reaction network structure, we study how such systems generate feature representations and assess their expressive...
Many societal challenges, including the diagnosis and treatment of disease, can be tackled by harnessing the unique capabilities of biology. A key goal in biomedical engineering is to improve human health via personalized biomedicine, which promises targeted treatment guided by the patient's physiology. To this end, I work to engineer and build programmable biological systems for autonomous...
Computational tasks can be implemented using biochemical species, such as DNA, RNA, lipids, and proteins, that interact through specially designed reaction networks. This research area encompasses biocomputing, molecular computing, and computing with reaction networks. The long-term vision is seamless integration of living biological systems, nanorobotics, and computing, with immediate...
