Real Time
Infectious Disease Intelligence

AI-powered precision diagnostics for faster, more accurate, and more actionable clinical decisions at the point of care.

The Problem

Pathogen Intelligence Is Underutilized Across Every Sector

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Healthcare Sector

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The Problem

  • ●AMR is accelerating, compromising patient outcomes and hospital operations.
  • ●Traditional microbiology is reactive — providing information only after a threat has manifested.
  • ●Genomic sequencing exists but lacks a knowledge-enriched analytics platform to make data actionable.
  • ●Infectious disease data is locked in silos instead of integrated across networks.

Pastør21 Pathogen Intelligence

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The Solution

  • ✓High-throughput sequencing for real-time surveillance of high-risk AMR bacterial strains.
  • ✓Explainable AI (XAI) Risk Scoring with clinically interpretable treatment impact and mutation data.
  • ✓Diagnostic reports designed for direct EHR integration to guide infection control and stewardship.
  • ✓Regional Bioinformatics Nodes co-located within large IDNs for faster turnaround.

Government & Defense

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The Problem

  • ●Incomplete pathogen data — current surveillance lacks global integration needed to predict cross-border spread.
  • ●Force readiness risks — deployed units lack real-time intelligence on local endemic bacterial threats.
  • ●Data sharing between allied defense systems, NATO commands, and public health agencies is delayed or non-secure.
  • ●Leadership lacks defensible, explainable AI-generated risk scores for command decision-making.

Global Bio-Command & Intelligence

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The Solution

  • ✓Alliance-Enabled Data Exchange: secure pipelines with NATO, CDC, and allied ministries to map AMR hotspots.
  • ✓Global Pathogen Watchlist: a comparative threat index ranking local and international prevalence and mutation trends.
  • ✓Modular Surveillance Units embedded in ID Centers of Excellence for near-base or in-theater validation.
  • ✓Scenario-Based Risk Models: downloadable playbooks with containment measures and treatment guidance.

Industry, Pharma & Supply Chain

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The Problem

  • ●Contamination blind spots — bacterial contamination can disrupt manufacturing of antibiotics, surgical kits, and PPE without warning.
  • ●R&D obsolescence — emerging resistance patterns can render drug candidates ineffective before they reach market.
  • ●Cross-border logistics risk — globalized production introduces environmental risks to API purity and yield.
  • ●Compliance gaps — companies lack real-time biosafety documentation and geographic intelligence on facility-level vectors.

Supply Chain Pathogen Surveillance

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The Solution

  • ✓Dynamic Disruption Simulators: models forecasting how AMR trends will impact supply nodes and critical manufacturing.
  • ✓Mutation Intelligence Feeds: tailored reporting for pharma teams to track pathogen evolution and inform R&D.
  • ✓Threat-to-Supply Dashboards: real-time alerts for sterile manufacturing sites and cold chain logistics hubs.
  • ✓Regulatory Compliance Tools: automated biosafety documentation for FDA, EMA, and CDSCO standards.

Today, those capabilities are disconnected across every sector.

The Solution

A Genomic Operating Layer for Infectious Disease

1

Pan-Genomic Sequencing

High-resolution pathogen analysis at scale.

2

Explainable AI (xAI)

Converts multi-omic data into clinician-ready insights.

3

EHR-Embedded Reporting

Structured, actionable reports delivered within clinical workflow.

4

Reimbursable Infrastructure

Designed for billing code adoption and health system ROI.

We do not replace microbiology labs.

We enhance them with intelligence.

How It Works

From Sample to Actionable Insight

1

Clinical sample collected

2

Pathogen genome sequenced

3

AI-driven resistance & virulence analysis

4

Structured, clinician-ready report generated

5

Integrated delivery into hospital systems

Output

  • ● Resistance prediction
  • ● Virulence risk assessment
  • ● Stewardship optimization guidance
  • ● Population-level surveillance capability

Turnaround

Designed for operational clinical timelines.

Initial Application

Antimicrobial Resistance & Stewardship Optimization

● Gene-level AMR detection
● Resistance prediction models
● Risk stratification for infection progression
● Antibiotic optimization support

Hospitals face regulatory pressure to demonstrate stewardship compliance.

Pastør21 provides measurable, reportable intelligence.

Value to Health Systems

Clinical + Operational + Financial Alignment

Clinical

  • ✓ More precise antibiotic selection
  • ✓ Reduced escalation risk
  • ✓ Data-driven infectious disease management

Operational

  • ✓ Standardized genomic reporting
  • ✓ Scalable pathogen intelligence
  • ✓ Real-time surveillance visibility

Financial

  • ✓ Reduced length of stay exposure
  • ✓ Antibiotic optimization savings
  • ✓ Stewardship compliance support
  • ✓ Reimbursable reporting model

Why Now

📉

Genomic cost curves have collapsed.

📈

AMR burden is increasing.

⚙

Stewardship mandates are expanding.

⚖

Hospitals are under margin pressure.

✅

AI infrastructure is mature enough for deployment.

Team

Scientific Depth. Health System Execution.

● World-class systems biology leadership
● Deep infectious disease genomics expertise
● Proven health system executive leadership
● Established global hospital partnerships

This is not a research project.

It is a clinical infrastructure build.

Bernhard

Bernhard Palsson

Co-Founder

Bernhard is an endowed Professor in the Department of Bioengineering at the UC San Diego. Dr. Palsson has co-authored more than 660 peer-reviewed research articles. His research includes the development of computational biology, genome-scale models, data analytic methods and the formulation of specific sysbio models of the red blood cell, E. coli, CHO cells, and many human pathogens. He is inventor on over 40 U.S. patents, the co-founder of several biotechnology companies, and holds several major biotechnology awards

Jonathan

Jonathan Monk

Co-Founder

Jonathan has spent 15+ years in bioinformatics and systems biology research, development and implementation. He has published over 100 peer-reviewed publications on applications of genomics for infectious disease diagnostics and management. Jonathan was previously funded on a $10M NIAID grant focused on systems biology of antibiotic resistance. He previously worked for Sinopia Biosciences and UC San Diego. Jonathan received his PhD from UC San Diego and his BSE from Princeton University

Rick

Rick Gannotta

Acting CEO

Biography. Dr. Richard Gannotta is a seasoned healthcare executive, educator, and innovator with extensive C-Suite experience leading major health systems, including UC Irvine Health, Duke Raleigh Hospital, and Northwestern Memorial Hospital.

Adil

Adil Mardinoglu

President

Professor Adil Mardinoglu is an expert in the field of Systems Medicine, Systems Biology, Computational Biology and Bioinformatics. He is a Professor of Systems Biology in the Centre for Host-Microbiome Interactions (CHMI), King's College London, UK where he leads a computational group. He also works as group leader in the Science for Life Laboratory (Scilifelab), KTH-Royal Institute of Technology in Sweden and leads a team of 25 researchers working in the area of computational biology, experimental biology and drug development.

Partners

Phenomel Longevity Logo Pastør21 is a Joint Venture with Phenomel Longevity, leveraging their worldwide hospital network and expertise in genomics to enhance our capabilities.

Patents & Publications

Patents

WO 2024/249933 A1 · PCT/US2024/032104

Identification of Marker Sequences for Rapid Classification of Microbial Pathogens Through Machine Learning Analysis of Genome Assemblies

WO 2024/233575 A1 · PCT/US2024/028202

Methods to Identify Novel Genetic Features Associated With a Targeted Phenotype by Discovery-Oriented Machine Learning

Provisional · 2026-052-2

Systems and Methods for Sparse Non-Negative Factorization of Large-Scale Genomic Data

Selected Publications

Structure of the Enterobacter Pan-Genome Is Revealed Using Machine Learning

Burrows, Li, Monk, Chauhan, Palsson

Microbiology Spectrum · 2026

Decomposition of the Pangenome Matrix Reveals a Structure in Gene Distribution in the Escherichia coli Species

Chauhan, Ardalani, Hyun, Monk, Phaneuf, Palsson

mSphere · 2025

Whole-Genome Sequences From Wild-Type and Laboratory-Evolved Strains Define the Alleleome and Establish Its Hallmarks

Catoiu, Phaneuf, Monk, Palsson

Proceedings of the National Academy of Sciences · 2023

Systems Biology Approach to Functionally Assess the Clostridioides difficile Pangenome Reveals Genetic Diversity With Discriminatory Power

Norsigian, Danhof, Brand, Midani, Broddrick, Savidge, Britton, Palsson, Spinler, Monk

Proceedings of the National Academy of Sciences · 2022

Comparative Pangenomics: Analysis of 12 Microbial Pathogen Pangenomes Reveals Conserved Global Structures of Genetic and Functional Diversity

Hyun, Monk, Palsson

BMC Genomics · 2022

Machine Learning With Random Subspace Ensembles Identifies Antimicrobial Resistance Determinants From Pan-Genomes of Three Pathogens

Hyun, Kavvas, Monk, Palsson

PLOS Computational Biology · 2020

A Biochemically-Interpretable Machine Learning Classifier for Microbial GWAS

Kavvas, Yang, Monk, Heckmann, Palsson

Nature Communications · 2020

Cellular Responses to Reactive Oxygen Species Are Predicted From Molecular Mechanisms

Yang, Mih, Anand, Park, Tan, Yurkovich, Monk, Lloyd, Sandberg, Seo, Kim, Sastry, Phaneuf, Gao, Broddrick, Chen, Heckmann, Szubin, Hefner, Feist, Palsson

Proceedings of the National Academy of Sciences · 2019

Machine Learning and Structural Analysis of Mycobacterium tuberculosis Pan-Genome Identifies Genetic Signatures of Antibiotic Resistance

Kavvas, Catoiu, Mih, Yurkovich, Seif, Dillon, Heckmann, Anand, Yang, Nizet, Monk, Palsson

Nature Communications · 2018

Genome-Scale Metabolic Reconstructions of Multiple Salmonella Strains Reveal Serovar-Specific Metabolic Traits

Seif, Kavvas, Lachance, Yurkovich, Nuccio, Fang, Catoiu, Raffatellu, Palsson, Monk

Nature Communications · 2018

iML1515, a Knowledgebase That Computes Escherichia coli Traits

Monk, Lloyd, Brunk, Mih, Sastry, King, Takeuchi, Nomura, Zhang, Mori, Feist, Palsson

Nature Biotechnology · 2017