CONSULTING
Computational Biology Expert AI Agent: a Roadmap
The Philancea Network is training AI Agents to perform specialized computational biology tasks. These agents will be "staffed" in-house to help Philancea deliver comprehensive results efficiently. Our agent network will accelerate delivery, consolidate vast knowledge, and keep operations lean.
Phase I: Simulating Cell Metabolism
Phil, our first agent, is training to become a cell simulation specialist. Upon completion, Phil will embody a powerful blend of extensive domain knowledge, mastery of specialized tools, and adaptability to evolving specifications. This unique combination will position Phil as an invaluable asset for delivering mission-critical, time-sensitive, and budget conscious projects in microbial biomanufacturing. Training comprises:

1

Proficiency in specialized libraries
Master Python-based constraint-based reconstruction and analysis via RAG and fine-tuning on curated compendium of code - prompt examples

2

Create new cell simulators and fill knowledge gaps
Create draft metabolic networks from annotated genomes. Use RAG to retrieve knowledge and fill gaps.

3

Expert problem solving
Automatically design comprehensive simulation projects to answer client needs. Set up simulations across numerous environments and genetic backgrounds, select appropriate microbes and communities.
Phase II: Multi-Scale Cell Simulators
ME Models
Integrated simulation of metabolism and protein expression
Multi-Scale Data Integration
Interpret multi-scale simulation data and generate comprehensive insights
Dynamic Bioprocess Simulations
Continuous, batch, fed-batch. Linking intracellular processes to bioprocess configurations and dynamics
Phase III: Multi-omics to Experiment Design
Interpret massive data
Rapidly interpret gigabytes to terabytes of simulated multi-omic data. Plot bioprocess and multi-omic data, tailored for researchers or stakeholders
Statistical and machine learning analyses
Perform clustering and dimensionality reduction, quality control, enrichment and differential expression analyses
Recommend Experiments
Active learning and optimal experiment design tools to recommend wetlab experiments to improve performance and/or optimize model confidence