Eman AhmedRutgers BME

Making polymer discovery an empirical search rather than a guess

Biomaterial performance comes from several structural properties interacting at once, which makes it a poor fit for one-variable-at-a-time chemistry. My work runs the search in parallel instead: plate-based synthesis, consistent measurement, and models trained on everything that comes back.

Polymer-stabilized enzymes in organic solvents

Doctoral project, Gormley Lab, 2022-2026

Enzymes are extraordinary catalysts in water and frequently useless outside it. Move one into a water-miscible organic solvent, which is often where the interesting synthetic chemistry happens, and it tends to unfold, aggregate and drop out of solution.

Random copolymers can act as synthetic chaperones, wrapping a protein in a shell whose chemistry can be tuned monomer by monomer. The difficulty is that the design space is enormous and the structure-function relationship is not obvious from first principles. My work builds plate-based, automated assays that measure solubility and retained activity across large copolymer libraries, so the search can be run empirically at scale rather than one rational design at a time.

  • Random copolymers
  • Polymer-enzyme hybrids
  • Biocatalysis
  • Solubility assays
  • Automation

In depth

Machine learning for biomaterial structure-function mapping

First-author review, Tissue Engineering Part A, 2024

High-throughput experiments produce the kind of data that models need: many conditions, consistent measurement, and, importantly, retained failures. The question is what to do with it.

My review in Tissue Engineering Part A surveys how machine learning is being used to identify the physicochemical cues that govern biomaterial performance, across tissue engineering, gene delivery, drug delivery, protein stabilization and antifouling surfaces, along with the data-mining approaches that let published results stand in for experiments that have not been run yet.

  • Structure-property relationships
  • Data mining
  • Model selection
  • Biomaterials discovery

In depth

Automated polymer synthesis and analysis

photo-ATRP platform and SAXS Assistant

Oxygen-tolerant reversible-deactivation radical polymerization made it possible to run controlled polymer chemistry in open labware. That is what puts it within reach of a liquid handler, and it is the premise behind our automated photo-ATRP platform, which screens ligands and initiators for monomers, methyl methacrylate among them, that are slow enough to be painful to optimise by hand.

Synthesis throughput is only useful if characterisation keeps up. SAXS Assistant addresses the other end of the pipeline: it automates small-angle X-ray scattering analysis, estimates maximum particle dimension with a model trained on 1,940 experimental profiles, and refuses to report results it cannot stand behind.

  • photo-ATRP
  • Liquid handling robotics
  • SAXS
  • Open-source tooling
  • Reproducibility

In depth

In depth

Four longer pieces on the methods: what each one does, the numbers reported in the papers, and where it stops working.

Methods and instrumentation

What I actually use day to day, at the bench and in code.

Polymer chemistry
Photoinduced ATRP, PET-RAFT, random copolymer synthesis, vacuum filtration, solubility characterisation
High-throughput experimentation
Liquid handling robotics, 96-well plate assay design, automated enzyme activity and solubility screening
Structural characterisation
Small-angle X-ray scattering, Guinier and P(r) analysis, Kratky interpretation, synchrotron data workflows
Computation
Python, scikit-learn, multilayer perceptron and Gaussian mixture models, data visualisation, experimental design automation
Biophysics
Protein stabilization, biocatalysis in water-miscible organic solvents, coarse-grained modelling of lipid membranes

Common questions

The questions I am asked most often about this work, answered directly.

What do you actually work on?

Three connected things. The doctoral project develops automated, plate-based assays that find random copolymers capable of keeping enzymes soluble and catalytically active in water-miscible organic solvents. Alongside it I work on automated photoinduced ATRP, which is what makes the polymer libraries possible in the first place, and on machine-learning-assisted analysis of small-angle X-ray scattering, which is how the products get characterised at that rate.

Why run experiments in parallel instead of one at a time?

Because biomaterial performance usually depends on several structural properties interacting at once, and varying one factor at a time samples that design space far too sparsely to find anything good. Running many formulations in parallel under identical measurement conditions also means the failures are recorded rather than discarded, and a dataset that contains the conditions that did not work is the one a model can actually learn from.

What is oxygen-tolerant ATRP, and why does it matter for automation?

Atom transfer radical polymerization traditionally needs oxygen-free conditions, which in practice means sealed, degassed glassware that cannot be parallelised. Oxygen-tolerant reversible-deactivation radical polymerization consumes the oxygen within the reaction system itself, so the chemistry runs in open labware such as a 96-well plate. That single change is what puts controlled polymer synthesis within reach of a liquid-handling robot.

What is SAXS Assistant?

An open-source Python tool I co-authored, published in Biophysical Journal, that automates small-angle X-ray scattering analysis. It extracts the Guinier radius of gyration, the pair distance distribution function, maximum particle dimension and Kratky features, and uses a multilayer perceptron trained on 1,940 experimental SASBDB profiles to estimate maximum particle dimension. The part I care about most is that it flags low-confidence results instead of reporting them silently.

Who do you work with?

I am advised by Adam J. Gormley in the Department of Biomedical Engineering at Rutgers, The State University of New Jersey. The Gormley Lab works on polymer-based biomaterials, high-throughput polymer synthesis and screening, and machine learning for biomaterial design.

Earlier work

Undergraduate research that led here.

2017–2018 · Neimark Lab, Rutgers

Tension-induced rupture of lipid membranes

Undergraduate research, Department of Chemical & Biochemical Engineering

  • Coarse-grained computational study of how lipid bilayers fail under applied tension.
  • Presented at the Gulf Coast Undergraduate Research Symposium, Rice University (2018).
  • Honourable mention, Aresty Undergraduate Research Symposium, Rutgers (2018).

2018 · Gormley Lab, Rutgers

Polymer characterisation

Undergraduate research, Department of Biomedical Engineering

  • Polymer vacuum filtration and solubility characterisation for polymer-based biomaterial systems.

Related reading

The three research areas above map onto three published papers. Each has a full record with the published abstract, identifiers and citation formats.

Publications Research notes