Lamellipodia are quasi-two-dimensional actin projections formed on the leading edge of the cell, playing an important role in sensing surrounding mechanical environments by forming focal adhesions. A branched actin network in the lamellipodia exhibits a stable, yet dynamic steady state characterized by a retrograde flow, which is attributed to a balance between network assembly at the leading edge and disassembly at the rear. Although the molecular players and architecture of the lamellipodia have been investigated extensively during recent decades, it still remains elusive how the dynamic steady state with continuous retrograde flow is achieved and robustly maintained. Using an agent-based computational model, we probed how physical interactions between subcellular components in the lamellipodia mediate and sustain the dynamic steady state. We simulated the branched network found in the lamellipodia, consisting of F-actin, myosin motor, Arp2/3 complex, and actin cross-linking protein, on an elastic substrate. We reproduced a steady retrograde flow induced by myosin activity and balanced by the interplay between network assembly and disassembly, but hindered by resistances from adhesions formed on the underlying substrate. We found that F-actin severing is crucial for maintaining a continuous, uniform retrograde flow because it enhances the disassembly of actin bundle/arc formed due to network contraction at the rear. In addition, we demonstrated that different modes of dynamic steady states are possible, and that a network which failed to show the retrograde flow due to perturbations can be rescued by altering other factors. Our study provides insights into understanding how cells maintain the dynamic steady state of the lamellipodia in highly varying microenvironments.

Large language models (LLMs) have reshaped the landscape of program synthesis. However, contemporary LLM-based code completion systems often hallucinate broken code because they lack appropriate code context, particularly when working with definitions that are neither in the training data nor near the cursor. This paper demonstrates that tighter integration with the type and binding structure of the programming language in use, as exposed by its language server, can help address this contextualization problem in a token-efficient manner. In short, we contend that AIs need IDEs, too! In particular, we integrate LLM code generation into the Hazel live program sketching environment. The Hazel Language Server is able to identify the type and typing context of the hole that the programmer is filling, with Hazel’s total syntax and type error correction ensuring that a meaningful program sketch is available whenever the developer requests a completion. This allows the system to prompt the LLM with codebase-wide contextual information that is not lexically local to the cursor, nor necessarily in the same file, but that is likely to be semantically local to the developer’s goal. Completions synthesized by the LLM are then iteratively refined via further dialog with the language server, which provides error localization and error messages. To evaluate these techniques, we introduce MVUBench, a dataset of model-view-update (MVU) web applications with accompanying unit tests that have been written from scratch to avoid data contamination, and that can easily be ported to new languages because they do not have large external library dependencies. These applications serve as challenge problems due to their extensive reliance on application-specific data structures. Through an ablation study, we examine the impact of contextualization with type definitions, function headers, and errors messages, individually and in combination. We find that contextualization with type definitions is particularly impactful. After introducing our ideas in the context of Hazel, a low-resource language, we duplicate our techniques and port MVUBench to TypeScript in order to validate the applicability of these methods to higher-resource mainstream languages. Finally, we outline ChatLSP, a conservative extension to the Language Server Protocol (LSP) that language servers can implement to expose capabilities that AI code completion systems of various designs can use to incorporate static context when generating prompts for an LLM.