Making immune complexity predictable and actionable

Our models represent immune pathways and interactions as executable biology. AI helps teams configure questions, run simulations, and interrogate results. The mechanistic layer keeps assumptions visible and makes predictions explainable.

Designed to connect mechanisms across scales as the model and evidence base expand.

1. 01

   Ask a question

   Start with the question, comparison, or decision point you need to understand.

2. 02

   Configure context

   Represent relevant biology, intervention assumptions, measurements, and population context.

3. 03

   Simulate mechanisms

   Mechanistic models represent the immune response as executable biology. AI-assisted tools help explore scenarios while the logic stays inspectable.

4. 04

   Inspect rationale

   See which mechanisms and assumptions drive each result, including uncertainty and alternative explanations.

5. 05

   Choose the next step

   Translate the result into the next experiment, measurement, or question.

Peer-reviewed research by ImmuNovus founder Tomáš Helikar and colleagues at the University of Nebraska–Lincoln and collaborating institutions demonstrates how mechanistic models can connect immune responses, identify therapeutic targets, and explain behavior across biological scales.

Explore the research →

• Modeling the connected immune system

  A mechanistic model spanning 11 disease contexts connects immune cells and secreted signals to simulate responses to infection, autoimmunity, and lung transplantation. The study evaluated its predictions in 38 validation experiments against published observations.

  11

  disease contexts

  1,450

  regulatory interactions

  38

  validation experiments

  Moore et al. · Frontiers in Immunology · 2026

  A comprehensive mechanistic multicellular model of the human immune system spanning 11 diseases

  Robert Moore, Lauren Mayo, Dennis Startsev, Bhanwar Lal Puniya, Kashish Poore, Resa Helikar, Tomáš Helikar

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• Identifying therapeutic targets

  Disease-specific metabolic models identified 68 candidate drug targets across three autoimmune diseases. In vitro experiments combined with published evidence supported CD4+ T-cell suppression for half of the identified targets.

  68

  candidate targets

  3

  autoimmune diseases

  Puniya et al. · npj Systems Biology and Applications · 2021

  Integrative computational approach identifies drug targets in CD4+ T-cell-mediated immune disorders

  Bhanwar Lal Puniya, Rada Amin, Bailee Lichter, Robert Moore, Alex Ciurej, Sydney J. Bennett, Ab Rauf Shah, Matteo Barberis, Tomáš Helikar

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• Connecting molecular mechanisms to immune behavior

  A CD4+ T-cell model integrated signaling, metabolism, cell populations, and cytokine dynamics using four modeling approaches. It reproduced published experimental behaviors and predicted emergent immune responses arising across scales.

  4

  modeling approaches

  3

  spatial scales

  Wertheim et al. · PLOS Computational Biology · 2021

  A multi-approach and multi-scale platform to model CD4+ T cells responding to infections

  Kenneth Y. Wertheim, Bhanwar Lal Puniya, Alyssa La Fleur, Ab Rauf Shah, Matteo Barberis, Tomáš Helikar

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