About

Professor William J. Evans is a faculty member at the Eddleman Quantum Institute at the University of California, Irvine. His research focuses on the synthesis of rare-earth metal compounds, single-molecule magnets, and molecular qubits. As the director of the institute, he contributes to advancing quantum science through interdisciplinary collaboration and education. His work is integral to the institute's concentrated effort on rare-earth metals, which are valuable in exploring the frontiers of quantum science and technology.

Research topics

• Cell biology
• Biology
• Genetics
• Biophysics
• Physics
• Computational biology

Selected publications

• bcberg/cytosim-bcb: v1.0.0

  Open MIND · 2026-03-11

  otherOpen accessSenior author

  Fork of Cytosim, adds applied force functionality

  PublisherDOI

• DNMT3s and TETs adjust CpG methylation canyon width to regulate gene expression and cell fate

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• bcberg/cytosim-bcb: v1.0.0

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-11

  otherOpen accessSenior author

  Fork of Cytosim, adds applied force functionality

  PublisherDOI

• Astral architecture can enhance mechanical strength of cytoskeletal networks by modulating percolation thresholds

  Biophysical Journal · 2026-03-31

  articleOpen accessSenior author

  A repeated pattern in cytoskeletal architecture is the aster, in which a number of F-actin filaments emerge star shaped from a central node. Aster-based structures occur in cytoplasmic actin, the early stages of the cytokinetic ring in yeast, and in the context of biomimetic materials engineering. In this work, we use computational simulation to show that there is an optimal number of filaments per aster that maximizes rigidity, even at a fixed density of F-actin. This nonlinear dependence holds for both the shear and extensional moduli. At physiological parameters, the maximum corresponds approximately to the same filaments per aster observed in recent super-resolution images of cortical F-actin. Furthermore, we find that increasing filaments per aster leads to dramatic increases in the sample-to-sample variability in network rigidity. We explain both effects using percolation theory, wherein the probability that a given network is productively connected exhibits a sharp dependence on parameters. The dependence of network rigidity on this nanoscale architectural feature may suggest a mechanism by which cells tune the physical properties of their actin networks locally and rapidly (since no new F-actin must be assembled) and may inform efforts to create adaptive synthetic metamaterials inspired by actin networks.

  PublisherDOI

• Tethered Signaling Proteins

  Annual Review of Biophysics · 2026-01-16

  article1st authorCorresponding

  Cells process signals by using large and complex networks of molecules that interact with and modify one another. Some of these interactions occur among molecules connected by long flexible tethers, often made of intrinsically disordered protein regions. In this review, we present recent research showing that tethered reactions ( a ) are ubiquitous in cells, ( b ) are exploited by cell signaling networks, ( c ) can be qualitatively and quantitatively understood using simple polymer physics, ( d ) give rise to categorically different features compared with molecular interactions driven by free diffusion, and ( e ) provide novel avenues for therapeutics and bioengineering. Recent studies have begun to shed light on cases in which the tethers must reach between different molecular assemblies that are not connected by protein scaffolding. We provide an in-depth case study of immune receptors, where such tethered signaling plays a vital role in signal integration and immune cell decisions.

  PublisherDOI

• BPS2025 - Computational simulation of astral cytoskeletal networks reveals optimal architecture for mechanical strength

  Biophysical Journal · 2025-02-01

  articleSenior author
  PublisherDOI

• Scalable inference and identifiability of kinetic parameters for transcriptional bursting from single cell data

  Bioinformatics · 2025-10-18 · 2 citations

  articleOpen access

  MOTIVATION: Stochastic gene expression and cell-to-cell heterogeneity have attracted increased interest in recent years, enabled by advances in single-cell measurement technologies. These studies are also increasingly complemented by quantitative biophysical modeling, often using the framework of stochastic biochemical kinetic models. However, inferring parameters for such models (i.e., the kinetic rates of biochemical reactions) remains a technical and computational challenge, particularly doing so in a manner that can leverage high-throughput single-cell sequencing data. RESULTS: In this work, we develop a chemical master equation model reference library-based computational pipeline to infer kinetic parameters describing noisy mRNA distributions from single-cell RNA sequencing data, using the commonly applied stochastic telegraph model. The approach fits kinetic parameters via steady-state distributions, as measured across a population of cells in snapshot data. Our pipeline also serves as a tool for comprehensive analysis of parameter identifiability, in both a priori (studying model properties in the absence of data) and a posteriori (in the context of a particular dataset) use-cases. The pipeline can perform both of these tasks, i.e. inference and identifiability analysis, in an efficient and scalable manner, and also serves to disentangle contributions to uncertainty in inferred parameters from experimental noise versus structural properties of the model. We found that for the telegraph model, the majority of the parameter space is not practically identifiable from single-cell RNA sequencing data, and low experimental capture rates worsen the identifiability. Our methodological framework could be extended to other data types in the fitting of small biochemical network models. AVAILABILITY AND IMPLEMENTATION: All code relevant to this work is available at https://github.com/Read-Lab-UCI/TelegraphLikelihoodInfer, archival DOI: https://doi.org/10.5281/zenodo.16915450.

  PublisherDOI

• Impact of N-terminal dimerization on formin homology 1 domain polymer dynamics and actin assembly

  Biophysical Journal · 2025-11-23

  articleOpen accessSenior authorCorresponding

  Abstract Many proteins contain intrinsically disordered regions (IDRs) that lack stable 3-dimensional structure. IDR behavior is poorly understood, leading to challenges for biochemical and computational analysis of IDR-containing proteins. Formins are a diverse set of homodimers containing an IDR — the FH1 domain — that facilitates polymerization of the cytoskeletal protein actin by increasing the local concentration of actin monomers at the actin assembly site. A commonly accepted model of formin-based actin polymerization involves a capture-and-deliver process: one or more binding sites (proline-rich motifs, PRMs) “capture” actin monomers and then “deliver” actin to the actin assembly site. There is evidence that formin FH1 domains are dimerized on both ends, but much research has been performed with formin constructs lacking the N-terminal dimerization site. Here, we ask: What happens when N-terminal dimerization is added to the standard model of formin-mediated actin assembly? We extend the kinetic model of FH1-mediated actin polymerization by incorporating a coarse-grain polymer model of FH1 domain dynamics, modeling the FH1 domain as a freely-jointed chain. We find that N-terminal dimerization can impact polymerization rates by modifying binding site accessibility and/or local concentration of binding sites (PRMs) at the actin assembly site (FH2 domain). Which effect dominates depends on kinetic parameters and formin properties such as FH1 domain length and binding site location. Additionally, we demonstrate that our model can be fit to experimental data and used to make predictions for the effects of N-terminal dimerization on a variety of formin family members. Significance Intrinsically disordered regions (IDRs) are common protein components that lack stable 3D structures and are thus difficult to study. Here, we develop a polymer-physics based computational model of the formin FH1 domain, an IDR involved in building the cell’s cytoskeleton. Some disease-associated mutations of formins occur in a region that is suspected to induce dimerization, forcing the FH1 domains to form a loop. Little is known about the loop’s prevalence, location, or impact on cytoskeletal assembly. Using simple polymer physics, we demonstrate that this dimerization can alter FH1 domain polymer dynamics and thus impact cytoskeletal assembly. These results not only highlight an important aspect of FH1-mediated cytoskeletal assembly, but also provide a framework for modeling IDRs.

  PublisherDOI

• Improvement in model flexibility reveals a minimal signaling pathway that explains T cell responses to pulsatile stimuli

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-02

  preprintOpen access1st authorCorresponding

  Abstract Efforts to develop quantitative models must face a trade-off between interpretability and quantitative accuracy, which often disfavors interpretability. Here we adopt an operational definition of interpretability, specifically that a model is described by an arrow diagram wherein each arrow corresponds to a positive effect or negative effect of one component upon a process, and fewer arrows is more inter-pretable than more arrows. We then develop a method to add flexibility — and thus accuracy in fitting data — to mathematical models by relaxing functional form assumptions, while constrained by the same arrow diagram and thus the same interpretability. We apply this method to the T cell, where quantitative models are particularly needed, in part because of ongoing efforts to engineer T cells as therapeutics. One avenue of experiments exposes T cells to pulsatile inputs and measures their frequency response, finding several nonlinear frequency responses: high-pass, low-pass, band-pass, and band-stop. Using our modeling approach with enhanced flexibility, we show that a simple signaling model quantitatively captures the frequency response of CD69 surface expression, one of the correlates of T cells activation, with accuracy within the experimental inter-replicate standard error of the mean. Specific qualitative behaviors map to specific parts of the arrow diagram: Band-pass behavior can be explained by refractory de-sensitizing circuit (we refer to this as “first-aid icing a wound”). Band-stop behavior can be explained by removal-inhibition (we refer to this as “roommate interrupts my studying”). Apparent low-pass emerges naturally when total stimulation time is constant. We test the model on independent experimental datasets from multiple labs. Taken together, our results demonstrate the ability to achieve both quantitative prediction and interpretability in understanding cellular dynamics. Simple models may at first appear incapable of explaining complex data, but might indeed be able to by adding this modest flexibility.

  PublisherOA PDFDOI

• Nucleosome placement and polymer mechanics explain genomic contacts on 100 kb scales

  Nucleic Acids Research · 2025-07-19

  articleOpen accessSenior author

  The 3D organization of the genome-in particular, which two regions of DNA are in contact with each other-plays a role in regulating gene expression. Several factors influence genome 3D organization. Nucleosomes (where ∼100 base pairs of DNA wrap around histone proteins) bend, twist, and compactify chromosomal DNA, altering its polymer mechanics. How much does the positioning of nucleosomes between gene loci influence contacts between those gene loci? And to what extent are polymer mechanics responsible for this? To address this question, we combine a stochastic polymer mechanics model of chromosomal DNA including twists and wrapping induced by nucleosomes with two data-driven pipelines. The first estimates nucleosome positioning from ATAC-seq data in regions of high accessibility. Most of the genome is low accessibility, so we combine this with a novel image analysis method that estimates the distribution of nucleosome spacing from electron microscopy data. There are no fit parameters in the biophysical model. We apply this method to IL-6, IL-15, CXCL9, and CXCL10, inflammatory marker genes in macrophages, before and after inflammatory stimulation, and compare the predictions with contacts measured by conformation capture experiments (4C-seq). We find that within a 500-kb genomic region, polymer mechanics with nucleosomes can explain 71% of close contacts. These results suggest that, while genome contacts on 100 kb scales are multifactorial, they may be amenable to mechanistic, physical explanation. Our work also highlights the role of nucleosomes, not just at the loci of interest, but between them, and not just the total number of nucleosomes, but their specific placement. The method generalizes to other genes, and can be used to address whether a contact is under active regulation by the cell (e.g. a macrophage during inflammatory stimulation).

  PublisherOA PDFDOI

Recent grants

• CAREER: Mathematical Framework for Elucidating Mechanics at Immune Cell Interfaces

  NSF · $469k · 2015–2022

• Control of cargo distributions by microtubule motor physical interactions with cargo, cytoplasm and MAPs

  NIH · $1.7M · 2017–2023

Frequent coauthors

• Omer Dushek

  University of Oxford

  20 shared

• Jesse Goyette

  EMBL Australia

  19 shared

• Steven P. Gross

  University of California, Irvine

  13 shared

• Matthew J. Bovyn

  Center for Systems Biology Dresden

  12 shared

• Lara Clemens

  University of California, Irvine

  11 shared

• Harvey Cantor

  Dana-Farber Cancer Institute

  9 shared

• Hans Wigzell

  Karolinska Institutet

  9 shared

• Gary J. Nabel

  OPKO Health (United States)

  9 shared

Labs

• Eddleman Quantum InstitutePI

Awards & honors

• Howard Lee awarded Humboldt Research Fellowship