Core Thrust
Simulation, Data Integration and Technology Support Structure
Core computational expertise, tools, techniques, data and modeling integrated and supporting the three primary biological thrusts.
Project C1: Shared-resource simulation tools
Objectives
Project C1 aims to develop a shared-resource platform hosting simulation tools for various scales (intracellular signaling to multicellular organization) and multi-modal biological cues.
Research Highlights
Digital cousins: Simultaneous optimization of one model for BMP signaling
Single core model captures BMP pathway in both Drosophila and zebrafish; small parameter changes explain differences.
Figure 9. Using multi-objective optimization, we calibrated a single core model to fit experimental data from both Drosophila and zebrafish.
Li, L., et al. 2025. Digital cousins: Simultaneous optimization of one model for BMP signaling. bioRxiv, 2025.02.25.640248v1.
Project C2: ML and AI Tools and Methods
Objectives
Project C2 develops machine learning and artificial intelligence tools to: (1) bridge scales from single cell to multicellular responses; (2) bridge experimental data and simulations across biological contexts.
Research Highlights
Optimal performance objectives in the highly conserved bone morphogenetic protein signaling pathway
BMP-Smad pathway balances competing functional goals; operates near Pareto optimal balance.
Figure 2. The Performance Metrics (PMs) of the Smad signaling pathway depend on the PPase concentration.
Shaikh, R., et al. 2024. Optimal performance objectives in the highly conserved bone morphogenetic protein signaling pathway. npj Systems Biology and Applications, 10(1), 103.
Project C3: Shared Imaging Measurement Systems
Objectives
Project C3 aims to develop shared measurement systems and protocols, including high-resolution imaging and atomic force microscopy (AFM).
Research Highlights
Probing multiplexed basal dendritic computations using two-photon 3D holographic uncaging
Dendritic nonlinearities enable "barcoding" through spike timing/firing rate control; sodium spikes crucial for pattern classification.
Figure 16. Multiplexing in basal dendrites of L5 PNs. A) Holographic two-photon uncaging. B) Precision and gain control by multibranch synaptic inputs. C) Barcoding of spatial-temporal input patterns in output spike trains.
Xiao, S., et al. 2024. Probing multiplexed basal dendritic computations using two-photon 3D holographic uncaging. Cell Reports, 43(7).