Madamanchi, A. | bioRxiv Science

bioRxiv2022

Computational modeling of TGF-β2:TβRI:TβRII receptor complex assembly as mediated by the TGF-β co-receptor betaglycan

Transforming growth factor-{beta}1, -{beta}2, and -{beta}3 (TGF-{beta}1, -{beta}2, and -{beta}3) are secreted signaling ligands that play essential roles in tissue development, tissue maintenance, immune response, and wound healing. TGF-{beta} homodimers signal by assembling a heterotetrameric complex comprised of two type I receptor (T{beta}RI):type II receptor (T{beta}RII) pairs. TGF-{beta}1 and TGF-{beta}3 signal with high potency due to their high affinity for T{beta}RII, which engenders high affinity binding of T{beta}RI through a composite TGF-{beta}:T{beta}RII binding interface. However, TGF-{beta}2 binds T{beta}RII 200-500 more weakly than T{beta}RII and signals with lower potency compared to TGF-{beta}1 and -{beta}3. Remarkably, potency of TGF-{beta}2 is increased to that of TGF-{beta}1 and -{beta}3 in the presence of an additional membrane-bound co-receptor, known as betaglycan (BG), even though betaglycan does not directly participate in the signaling mechanism and is displaced as the signaling receptors, T{beta}RI and T{beta}RII, bind. To determine the role of betaglycan in the potentiation of TGF-{beta}2 signaling, we developed deterministic computational models with different modes of betaglycan binding and varying cooperativity between receptor subtypes. The models, which were developed using published kinetic rate constants for known quantities and optimization to determine unknown quantities, identified conditions for selective enhancement of TGF-{beta}2 signaling. The models provide support for additional receptor binding cooperativity that has been hypothesized, but not evaluated in the literature. The models further showed that betaglycan binding to TGF-{beta}2 ligand through two domains provides an effective mechanism for transfer to the signaling receptors that has been tuned to efficiently promote assembly of the TGF-{beta}2(T{beta}RII)2(T{beta}RI)2 signaling complex.

biophysics↗

bioRxiv2019

Supporting Computational Apprenticeship through educational and software infrastructure. A case study in a mathematical oncology research lab

There is growing awareness of the need for mathematics and computing to quantitatively understand the complex dynamics and feedbacks in the life sciences. Although several institutions and research groups are conducting pioneering multidisciplinary research, communication and education across fields remains a bottleneck. The opportunity is ripe for using education research-supported mechanisms of cross-disciplinary training at the intersection of mathematics, computation and biology. This case study uses the computational apprenticeship theoretical framework to describe the efforts of a computational biology lab to rapidly prototype, test, and refine a mentorship infrastructure for undergraduate research experiences. We describe the challenges, benefits, and lessons learned, as well as the utility of the computational apprenticeship framework in supporting computational/math students learning and contributing to biology, and biologists in learning computational methods. We also explore implications for undergraduate classroom instruction, and cross-disciplinary scientific communication.

scientific communication and education↗