Optimal Computational Tools for Your Situation

Optimal Computational Tools for Your Situation
Here's a simple roadmap to organize yourself to apply the ideal modeling tools depending on where your technology is.
LY
by Laurence Yang
 
STEP 1
Determine My Situation
In what stage am I operating?
1
Stage 1: Proof of Concept
In this critical initial phase, we're developing a proof of concept to validate our approach's potential.
Demonstrating good growth with new strains and culture conditions is a primary concern. Even at this stage, integrating predictive models can differentiate our technology.
2
Stage 2: Growth and Optimization
As we progress, our focus shifts to optimizing performance at increasing scales and observing tangible improvements across strain engineering iterations. 
Strain and process improvements are becoming less obvious and data sets are becoming larger.
3
Stage 3: Industrial-Scale Production
At this advanced stage, we're operating at massive scales (tens to hundreds of thousands of tonnes/year). Even minor productivity increases can translate to millions in additional profit. 
However, complex strain designs with dozens of gene edits present significant challenges for further improvement.
STEP 2
Identify the Right Tools
Identify the right tools for my situation. Each stage has milestones and technical challenges.
For example, as productivity approaches the theoretical maximum, strain complexity (including interactions between gene edits) increases exponentially. Consequentially, improving performance further becomes increasingly difficult without leveraging high-throughput data and models capable of simulating numerous strains in silico to identify the most promising combinations.
Appropriate tools aligned with goals can accelerate overcoming these challenges…
Stage 1: Proof of Concept
Goals
Establish growth in baseline media
Identify unique phenotypes affecting production
Select optimal base strain
Tools
Genome-scale modeling
Machine learning (ML) analysis of phenotypic data
Omic data (e.g., transcriptomics)
Basic bioreactor simulation (e.g., dFBA, pc-dFBA)
Stage 2: Performance Optimization
Goals
Rapidly learn from experimental data
Validate strain engineering through iterations
Expand IP portfolio (strains, know-how, patents)
Tools
Design of Experiments
ML models trained on multi-omic data
Multi-scale constraint-based models (e.g., ME-models)
Stage 3: Industrial Scale Production
Goals
Explore beyond known genes
Implement advanced AI/ML techniques
Investigate unexplored mechanisms
Integrate strain and process engineering
Tools
Advanced AI/ML for strain optimization
LLMs for DNA, RNA, protein structure & function
Extended multi-scale models (e.g., stress response, dynamic multi-scale)
Computational fluid dynamics for large-scale bioreactors
Digital twin of bioprocess
STEP 3
Implement, Optimize, and Iterate to Scale
Data and Knowledge Management
Establish protocols and infrastructure for data and knowledge management – investing too late in this infrastructure can be costly as deep knowledge becomes segregated and pivoting products and processes becomes costly and slow.
Team Training
Continually invest in team training - workshops, coaching, advisory consulting are cost-effective ways to stay afloat of rapid technological advances in a fast-moving industry.
Performance Tracking
Define key performance indicators (KPI) to measure the impact of tools. Implement a system to track improvements in strain performance, design efficiency, and regularly assess ROI of tools.
Interested in exploring what computational tools are optimal for your specific situation?
In a short and impactful conversation I'll share with you:
how to monitor whether you're getting the most out of your bioprocess and multi-omic data to accelerate strain and process improvement
how to validate in weeks whether an AI or simulation platform will benefit you, instead of in months to years
how to identify which AI and modeling tools best match your specific situation
Book a free consultation