Redefining Bacterial Mechanics

Driving Microorganism
Mechanical Adaptation

A discovery platform for characterizing and manipulating bacterial cell mechanics to solve complex challenges in Biomedicine, Animal Nutrition, and Biometallurgy.

Mission

A Silent Crisis

Current antibiotics often fail not because bacteria are genetically resistant, but because they tolerate stresses. In chronic conditions like Cystic Fibrosis and COPD, pathogens like M. abscessus and P. aeruginosa modify cell physiological properties to optimize tolerance to host immunity and conventional antibiotics.

The approach taken to develop new antibiotics has focused almost exclusively on targeting discrete molecular mechanisms that are believed to be essential systems for life and virulence. However, evolution and adaptation readily permit bacteria to circumvent such chemotherapeutic strategies. We therefore propose a new paradigm for drug discovery, one that bacteria cannot overcome to cheat death.

  • Targeting global markets in health, nutrition, and industry
  • Discovery of compounds driving mechanical change
  • Harnessing mechanical change in evolutionarily disparate species
High-Resolution Microscopy of Bacterial Biofilm
Science

Mechanical Morphotype Switching

We have discovered that bacterial pathogens actively switch into differentiated mechanical morphotypes affecting survival during host cell infection. The trajectory of switching into discrete mechanical morphotypes dictates whether bacterial pathogens will exhibit enhanced tolerance to antibiotics and host innate immunity.

Expanding on research published in Science Advances (Eskandarian et al. 2024)

Human & Animal Health

Characterizing chemical compounds that drive mechanical change to treat infections and improve physiological outcomes in both human and animal health industries.

Biometallurgy

Directing the mechanics of chemilithoautotrophs for efficient bioleaching, mineral purification, and industrial-scale microorganism adaptation.

Genetic Control

Specific bacterial genes regulate the mechanical cell state. Identifying these genes has provided us the ability to uncover new therapeutic targets.

AI Discovery Engine for Bacterial Mechanics
Platform

AI Discovery Engine

We integrate high-throughput biophysical profiling with generative AI to identify compounds that modulate the mechanical states enabling bacterial tolerance.

Generative AI Models

Utilizing Chemical Transformers to predict small molecules that target the genes regulating mechanical morphotypes.

Biological Validation

Validating lead candidates in host-relevant infection models to confirm attenuation of persistence and tolerance.

High-Throughput Sorting

Rapidly characterizing changes in cell stiffness and density within large compound libraries to identify mechanical hits.

Approach

Defining a New Paradigm for Treating Bacterial Infection

While antibiotics are expected to reduce bacterial loads directly by killing or blocking bacterial cell replication, the selective pressures added drive for the emergence of evolved mutants that are resistant and consequently to replace the original population. Our approach focuses on modulating bacterial cell mechanical properties, for which the mechanical cell state represents a fundamental cell property enabling survival. Virulence is inherently dependent on the mechanical nature of the cell and if modified the bacterium is rendered susceptible to killing.

Characterized
Compound Discovery
General
Mechanism of Action
Cross
Sector Applications

Leadership Team

Combining expertise in microbiology, computational neuroscience, and business operations.

Vahan Manukyan

Vahan Manukyan

Business Lead | CFA, MBA

Co-Founder of multiple tech startups. Extensive experience leading finance & operations for VC-backed companies.

Haig A. Eskandarian

Haig A. Eskandarian

Science Lead | PhD

Microbiologist & Host-Pathogen specialist. Institut Pasteur, EPFL, UCSF, Harvard University.

Mehran Spitmann

Mehran Spitmann

AI Lead | PhD

Computational Neuroscientist. Expert in AI models for biomarker discovery and computational modeling