Systems biology · MIDD · QSP

Systems biology platform for model-informed drug development

Edik Blais founded ggomics to provide biopharma with mechanistic systems biology models (e.g. signaling dynamics, metabolic networks, spatial microenvironments) and digital twin services:

  1. Distill multi-omic data into mechanisms clinicians can trust
  2. Power up biomarker data with multi-scale network simulations
  3. Get regulatory submissions ahead of the AIxBio curve

Purpose-built systems biology models for disease-specific programs

ggomics is on a mission to transform how biomarker data are communicated to clinical stakeholders throughout the development cycle

MULTI-SCALEmulticellular interactionsmetabolic networkssignaling ODEsMULTI-OMICDNA · scRNA · spatialprotein · cytokinemetaboliteDISEASE-SPECIFICcardiopulmonaryimmune & metaboliconcology

Where MIDD is headed

The NIH and FDA actively encourage innovation in multi-scale modeling, digital twins, and MIDD infrastructure.

ggomics is open to academic and industry collaborations to advance efforts aligned with these initiatives:

These external links open a new tab to NIH.gov and FDA.gov pages that describe similar modeling approaches that ggomics supports.

Roadmap

Pilot project timeline

1

Project kickoff and disease scoping meetings

2

Knowledgebase architecture deployed to client cloud

Public data sourcing and parameterization

Custom bioinformatics pipelines

Comparative multi-omic visualizations

3

Core signaling/metabolic networks delivered for pilot disease/therapy area

4

Publication & strategy meetings based on insights from core model simulations

5

The ggomics platform continues to support your growing pipeline

Multi-scale validation with new omics data

Model expansion into new disease areas

Extension for QSP/MIDD applications

The ggomics simulation triad: systems biology simulations — multi-scale mechanistic models of perturbation response pathways — built from signaling dynamics, metabolic networks, and spatial cell–cell crosstalk. Perturbation comparisons drive the models: disease vs healthy, therapy vs control, CRISPRi vs wildtype, and CRISPRa vs baseline. Underpinning it is a knowledgebase architecture of multi-omic pipelines and visualizations over structured public and private data.

ggomics delivers mechanistic insights at strategic meetings

ggomics deploys modeling infrastructure on your cloud so teams can:

generate mechanistic network predictions
visualize signaling and metabolic activities
compare private/public molecular datasets
source data and customize bioinformatics workflows

multi-scale mechanistic modeling powered by ggomics

Signaling networks simulate dynamic responses to perturbations using systems of Ordinary Differential Equations (ODEs) at the cellular level.
Expression and activiation of receptors, kinases, transcriptional regulators, and feedback loops govern treatment durability and mechanisms of resistance.
Metabolic networks are optimized to predict the effects of perturbations on cellular growth, energy maintenance, and biomarker production.
Flux Balance Analysis (FBA) of stoichiometric biochemical and transport reactions are constrained by mass balance and nutrient uptake.
Microenvironment networks capture complex interactions between cell types and cell states using single cell and spatial data.
Agent-Based Modeling (ABM) integrates dynamic gradients and multicellular crosstalk between signaling and metabolic models across tissue layers and organoid models.

Iteratively curated networks are continually validated to reproduce biological tasks and minimize overfitting

multi-omic integration strategies to up your MIDD/QSP game

Mechanistic models are built to integrate bulk gene expression, scRNA-seq, spatial transcriptomics, and proteomics datasets
Gene, protein, and metabolite expression changes support biomarker discovery with their predicted contributions to pathway-specific tasks.
Network models simulate multi-perturbation screens for conditionally essential genes, synergistic targets, and resistance pathways.
Digital twin predictions help prioritize CRISPRi/a experiments for validation, personalized therapies including unseen combinations, and innovative trial designs.
Model objectives are scoped by the study team to capture disease-relevant biology and treatment-related pathways.
Investigate the molecular underpinnings of disease biology and treatment response throughout preclinical and clinical development with ggomics.

Imaging, structural, and functional traits can further strengthen multi-omic links between disease biology and clinical activity

Purpose-built mechanistic modeling platform for your disease-specific program

Discuss a pilot project on your first call

Model objectives are scoped by the study team to capture disease-relevant biology and treatment-related pathways.

Book a scoping call

Explore Edik's publications by topic