Rob Phillips

The paper explores exploratory dynamics in biological processes, proposing mathematical frameworks like geometric distribution and random walks with resets to model search processes and their outcomes.

The paper measures ATP gradients in motor-microtubule assemblies and shows they are sustained by energy consumption, with implications for cytoskeletal organization.

The paper introduces an algorithm to identify condition-dependent transcription factor binding sites by compressing promoter sequences into hyperletters, validated on E. coli data.

This paper uses statistical mechanical models to study how endogenous signaling parameters, such as effector concentrations, influence the stability of genetic circuits in living cells, contrasting with traditional approaches that rely on Hill functions and manually tuned parameters.

The paper proposes exploratory dynamics as a framework for understanding biological processes that achieve specific outcomes through repeated, random trajectories.

The paper develops a cell-free platform to study how kinesin motor sequences influence microtubule dynamics, identifying distinct phenotypes and showing that recombination can alter dynamics.

This paper shows that increasing microtubule bundle length in active networks triggers a percolation transition, enabling global force generation and mechanical work.

The paper investigates motor-driven microtubule network contraction and identifies a diffusive-like spread within the network, showing a conserved relationship between contraction rates and diffusion constants.

The paper identifies regulatory elements and transcription factors in E. coli genes across diverse environments using experimental and computational methods.

The paper develops a mathematical model showing microbial proteins adapt to maintain redox balance, improving growth by aligning biomass redox state with nutrients.

The paper uses machine learning to identify individuals based on phageprint profiles from oral microbiomes, finding stable and unique phageprints per individual.

The paper develops a method to generate variant libraries in vivo using base editing to study gene regulatory programs in multicellular organisms.

The paper creates synthetic single-cell gene expression data using thermodynamic models to better understand and optimize MPRA experiments for regulatory architecture inference.

The paper profiles 8,269 IPTG-inducible promoters to model gene expression and identify optimal promoter architectures for predictable transcriptional control.

The paper quantifies the relationship between bacterial growth rate, cell size, and protein content in E. coli using proteomic data, finding ribosome biogenesis as a primary determinant of growth rate.

This study uses targeted sequencing of oral phage communities to show that phage compositions are individual-specific, stable over time, and not strongly influenced by geography or genetics.