Kristin Gleitsman

Leadership in Biotech

Tag: biological models

Illustration of a desk with a figure from a recent paper about Platinum Pedigree

AI Models Need Better Truth—Platinum Pedigree Shows How

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On November 29, 2025

In AI ∩ Bio

When I was in high school, I was obsessed with genetics. The Human Genome Project was in full swing, and it felt like the future was being written in real time. I told a family friend I wanted to become a geneticist. He smiled and said, “My cousin is at the NIH. They’ll finish the human genome before you finish college, so I wouldn’t bother.”

The project wrapped in 2003. But papers like this remind me how wrong that prediction was. Even after “finishing” the genome, we’re still uncovering what accuracy, completeness, and truth really mean.

The new Platinum Pedigree study pushes that frontier again.

Scientific Insight

This work builds one of the most comprehensive germline variant benchmarks to date, deep long-read sequencing across a 10-member family, combined with Mendelian logic.

By integrating PacBio HiFi, Oxford Nanopore Technologies, and Illumina and testing every variant against inheritance patterns, the authors defined 2.77 Gb of high-confidence genome (~200 Mb beyond prior benchmarks), including repeats, segmental duplications, and low-mappability regions.

The key innovation is biological grounding.

Each child inherits one haplotype from each parent; variants that obey those segregation patterns are kept, and those that don’t are removed. This yielded ~4.7M SNVs, 768k indels, 537k tandem repeats, and 24k structural variants as pedigree-consistent truth.

When DeepVariant was retrained on this truth set, error rates dropped by ~34% across challenging classes, especially indels and tandem repeats.

Better labels → better models.

Leadership Angle

For diagnostics leaders, this signals where the field is heading: stronger evidence standards, clearer definitions of “truth,” and biologically informed benchmarks rather than technology-constrained heuristics.

This strategy of combining multiple sequencing technologies and adjudicating discrepancies with inheritance is exactly how robust systems are built in uncertain environments.

It mirrors what clinical diagnostics now requires: pipelines that perform not just in easy regions, but in messy, clinically meaningful ones.

And it underscores a central lesson in AI-enabled diagnostics: your model is only as good as the ground truth you train it on.

The regions that are currently messy and difficult to map: that’s where new breakthroughs in understanding will occur.

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

Mentorship Angle

For early-career scientists, the lesson is craftsmanship. This paper doesn’t debut a flashy algorithm; it elevates the foundations. It asks simple but profound questions: Did this variant follow the rules of inheritance? If not, are we sure it’s real?

Your technical tools matter, but your willingness to interrogate assumptions matters more. If you want to build a meaningful career in genetics in this age of AI, stay curious about the scaffolding beneath the science.

Breakthroughs often start there.

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Illustration of a desk with a figure from a recent paper about Virtual Cell Grammar

Readable Rules, Testable Models: A New Grammar for Virtual Cells

By

On September 10, 2025

In AI ∩ Bio

Virtual Cell Models are all the rage right now, and this week’s AI ∩ Bio paper covers one recent paper that aims to democratize this approach by encoding complex multicellular dynamics in plain language.

This paper introduces a plain-language “cell behavior hypothesis grammar” that turns rules like “oxygen decreases necrosis” into executable agent-based models. The aim is to let researchers build virtual experiments directly from human-readable statements, initialize them with single-cell or spatial data, and test how cell–cell and tissue dynamics unfold.
Why it matters: it makes modeling more accessible, assumptions more transparent, and experiments easier to prioritize.

Scientific Insight

At its core, the grammar provides dictionaries of signals (what cells sense) and behaviors (what cells do), plus simple response forms, so a one-line rule becomes math the simulator can execute. The paper shows this through diverse examples: hypoxic tumor growth, PDAC invasion seeded from Visium data, tumor–immune dynamics, an EGF “go vs. grow” test validated with organoids and cell tracking, and cortical layer formation modeled from asymmetric division rules.

If you’re new to this, the big idea is: start with rules you can read, tie parameters to data where possible, and test which parameters truly drive outcomes.

Leadership Angle

For diagnostics and translational leaders, this work is a pragmatic step toward virtual cell laboratories: models initialized from tissue data can be used to explore therapy combinations and microenvironmental dynamics before committing wet-lab time. The scope is still local-tissue, not clinical, but when used carefully these grammars act as prioritization engines, or tools to sharpen questions and rank hypotheses.

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

Lessons for Early-Career Scientists

If you’re early in your career, I would consider two lessons here.

  • First, write the biological models down explicitly, framing for yourself how outcomes shift when parameters are perturbed.
  • Second, design experiments that rigorously interrogate these models: not to confirm them, but to expose where they fail.

Transparent rulebooks, sensitivity analyses, and reproducible code will accelerate your science regardless of your toolkit.

Virtual Cell Grammar formalizes implicit scientific thinking, but its value ultimately depends on how it’s used to design falsifiable experiments.

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