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Computational evolutionary model to predict the efficacy of broadly neutralizing antibodies (bNAbs) in suppressing HIV and to design optimal combination therapy with bNAbs.

The NeuroML model description language, with its extensive software ecosystem, supports researchers in the development of FAIR, data-driven, biologically detailed models of neural systems.

Short peptides that bind tightly to anti-apoptotic protein Bfl-1 but not other Bcl-2 family members provide a tool for diagnosing cancer cell survival mechanisms and a lead for developing new therapeutics.

A citizen science game reveals play interest patterns that could inform crowdsourcing experiment design.

A dimensional approach to psychiatry demonstrates the specificity and generalizability of a neurocognitive marker of compulsive behavior and intrusive thought via large-scale online testing.

Artificial neural networks can faithfully recapitulate realistic neuronal behavior with dramatically accelerated simulation runtimes, empowering accessible large-scale realistic network simulations.

Challenging a popular theory in neuroeconomics, a computational cognitive study provides evidence against divisive normalization, a supposedly canonical neural computation, in favor of an alternative account, range normalization, in the context of value learning.

MotorNet is a Python toolbox for training artificial neural networks to control arbitrarily complex, differentiable, and biomechanically realistic musculo-skeletal effectors on user-defined sensorimotor tasks.

aLENS implements new parallel methods to resolve cytoskeletal assembly dynamics with high stability, physical consistency, and scalability.

Novel designed alpha-sheet peptides inhibit amyloidosis in two different systems and preferentially bind the toxic oligomer.