Building a Visual Analysis Tools for Microbiologists
At Pytri, I led the end-to-end design of a platform that helps microbiologists measure bacterial colony growth under varying environmental conditions. My process included conducting user research, identifying three core growth principles, ideating solutions, wireframing, prototyping, running usability tests, and handing off final designs to developers.
Responsibilities
User Interviews
UX Research
Information Architecture
Design systems
UI Design
Prototyping
Usability testing
Role
Lead Product Designer
Solution Preview
Visual Image Analysis
A powerful and intuitive image editor helps researchers inspect, correct, and validate colony detection on petri dish images.
Organized Metadata Input
Clean, structured forms help users easily log experiment variables like stressor types, growth media, and replicate numbers.
Collaboration & Analysis support
Users can share projects with collaborators and track changes through detailed version histories.
Establishing a Research Driven Design Approach
To design responsibly in a complex field, I needed to deeply understand the workflows of microbiologists. I relied on several research methods to help inform my design process:
User Interviews
I interviewed 10 researchers to understand their goals, lab protocols, and frustrations with existing tools.
Competitor Analysis
I benchmarked lab data management tools like Benchling and LabArchives to identify gaps in user experience.
User testing
I ran usability tests with real researchers using early prototypes to validate ideas and uncover friction points.
Stakeholder workshops
Weekly syncs with the CEO, CTO, and biology team helped align priorities and constraints.
Workflow Analysis
I documented experiment pipelines to design features around real user needs. This informed key platform design decisions around dashboard structure and metadata fields.
Deriving Design Priorities from User Workflows
After conducting interviews and workflow observations with researchers from university labs and hospital research teams. I created a ideal user persona who falls directly into our user group. I focused on one representative research question which allowed me to map a realistic end-to-end workflow that included both in-lab activities (growing and exposing bacteria) and digital activities (data logging, image analysis, and result reporting).
Meet Dr. Elena Rao
Role: Post Doctoral researcher in Microbial Resistance
Institution: University hospital infectious disease lab
Experience: Comfortable with digital lab notebooks, basic scripting (Python/R), statistical software (GraphPad, SPSS)
Research Question:
Do bacteria grow differently when exposed to different types of stress, like UV light, heat, or pH changes?
I focused on one representative research question which allowed me to map a realistic end-to-end workflow that included both in-lab activities (growing and exposing bacteria) and digital activities (data logging, image analysis, and result reporting).
Elena’s Expectations: Identifying growth areas
By mapping out the user flow I was able to identify several user expectations which I then categorized into the following
Sample management
Ensure every sample and experiment has structured, searchable, and detailed documentation.
Project oversight & visualization
Give users visibility into experiment timelines, sample status, and overlapping projects.
Data import & analysis support
Make it easy to track and analyze complex experimental data, especially gene expression.
Collaboration tools
Enable research teams to work together efficiently and track collaborative input.
Data Integrity & Auditability
Maintain research transparency and reproducibility with strong data tracking.
Measuring methods
Methods that help researchers get data without tedious counting
Exploring and validating workflows & wireframes
I created a workflow map to help visualize how a user would integrate the Pytri platform into their existing experimental process. This allowed me to better understand and explain user needs and identify how our platform would provide the most value.
Keeping this workflow in mind I began to draft some low fidelity wireframes, which allowed me to quickly explore feature ideas and layout concepts that aligned with the established user goals. I shared wireframes with business and technical teams in order to discuss feasibility and limitations with features I ideated, before diving deep into the details.
Final Designs
Visual Image Analysis
Once users upload their images an intuitive editor lets researchers inspect and adjust automated colony detection on petri dish scans.
Users can fine-tune detection settings using a flexible toolbar, correct errors directly on the image, and simultaneously edit metadata making the process fast, accurate, and customizable.
Organized Metadata Input
Structured forms make it easy to log essential variables like treatment type, replicate ID, and media used.
By integrating metadata entry into the analysis workflow, users stay organized without interrupting their process reducing errors and improving traceability.
Collaboration & Analysis support
Teams can work together in real time by sharing projects and tracking changes across trials. Features like version history and role-based sharing allow researchers to trace decisions, compare results, and collaborate efficiently all without compromising data integrity.
Reflections & Impact
This project taught me how powerful design can be in translating complex scientific workflows into intuitive, usable tools. Coming in with limited knowledge of microbiology, I had to continuously break down unfamiliar concepts, adapt quickly to feedback, and iterate based on new findings. Through close collaboration with researchers, I learned how to build interfaces that support both students and seasoned scientists in order to bridge the gap between experimental complexity and usability.
Impact
Enabled faster, more accurate image analysis through clear, guided workflows.
Streamlined metadata entry, reducing manual errors in high-throughput research labs.
Supported more consistent, repeatable trials by structuring how experimental data is captured and organized.