Healthcare Professionals
Healthcare Professionals

Immuno-Engineering from the Numbers

Scientists Use Math to Back-Calculate Immune Responses
by Nick Miller

Today’s mega-computers and genomic sequencing technologies let scientists analyze the basic building blocks of life in ways unimagined two decades ago. Even so, learning how to exert precise control over the body’s immune system still relies on an old but useful practice—conceptualizing mathematical models on a drawing board.

Emily Miraldi

Emily Miraldi, PhD

Emily Miraldi, PhD, is a computational and systems biologist who joined the divisions of Immunobiology and Biomedical Informatics in January. Coming from New York University and the Simons Foundation, the MIT-trained scientist focuses on immuno-engineering: altering the behavior of specific immune cell populations during disease without compromising the body’s normal immune function. 

Her team is helping develop more precise therapies to ramp up immune cells and battle cancer, or turn them down to prevent autoimmune disease, while not interfering with healthy immune function. 

“There is no cure for autoimmune disease, so people are given immunosuppressive therapy,” Miraldi explains. “This is akin to using a sledgehammer on the immune response, leaving patients susceptible to garden variety infections.”

Genomics technologies provide high-dimensional snapshots of the cellular molecules (DNA, RNA, proteins, metabolites) that drive cell behavior.  Mathematical modeling can stitch these snapshots together into a blueprint of how combinations of molecules work together to orchestrate responses.  Miraldi and her colleagues use these models to predict the effect of molecular interventions (genetic, diet, drugs) on individual cell types. 

“This takes us closer to the goal of designing therapies to target a desired cell type, while leaving other cells of the body undisturbed,” she explains.

Tag Team Science

Miraldi works closely with experimental immunologists who study immune cells in living biological systems, such as cell cultures and mouse models. The experimentalists provide mass quantities of multivariate measurements on how cells behave – what they do, when they do it, where they do it, and what genes are expressed. Using computational methods conceived on a drawing-board, she leverages the numbers and patterns in this high-dimensional data to develop a mathematics-based hypotheses on the why. 

The model hypotheses go back to experimentalists. After more experimental testing, new lab data comes back to Miraldi and, in a continuous feedback loop, the process repeats until answers are found.

Truth in Transcription

Except for red blood cells and thrombocytes, every cell in the body has a nucleus with DNA that provides the blueprint for making any other cell in the body.  An outstanding question in biology is understanding how different cell types use only those parts of the blueprint needed to make that cell type.

The biophysics of the problem is fascinating: how to organize two meters of DNA in a 6-micron nucleus to get the right gene expression patterns in the right cell?  New biotechnologies make it possible to address this question on a genome scale, according to Miraldi. 

Her work in recent years has focused on developing mathematical frameworks to integrate two genomic measurement types. One is RNA-seq—snapshots of all genes expressed in a cell type. The other is ATAC-seq—snapshots that provide hints about which parts of the DNA could be used for proteins called transcription factors, which bind and influence gene expression. The resulting model is called a transcriptional regulatory network. It identifies the hundreds of transcription factors that combine to control the expression of thousands of genes in a particular cell in the body. 

“There are many immune cells of the body that have been studied for decades, and these new technologies combined with mathematical approaches have expanded our ability to develop a broader, more nuanced understanding—even in cell types with a rich research history,” Miraldi says. “They also provide an opportunity to benchmark the quality of my modeling approaches.”

While Miraldi uses vast sets of high-dimensional data to identify complex transcriptional networks—with hundreds or thou-sands of components—other researchers dive much deeper into much smaller handfuls of transcription factors, explains Harinder Singh, PhD, Director of Immunobiology. 

"So while we work from the ground up to assemble networks that are small-scale, Emily is working from the other direction. The reason Emily was recruited was to enable this kind of convergence between the top-down and bottom-up approaches," he says.

Exploring a New Cell

systems biology.

New faculty member Emily Miraldi, PhD, uses transcriptional regulatory network maps such as this, and other tools, to build mathematical models of immune cell behavior.

Miraldi’s most recent foray is inferring transcriptional regulatory networks in innate lymphoid cells (ILCs). The cells were discovered within the last decade, so many of the key drivers of their gene expression and behavior are unknown. 

ILCs can be broken into five subtypes, each with unique contributions to host defense, whether against viruses or fungi, etc., and autoimmune disease.

The research team now has models for each of the five ILC subtypes in the small intestine, and they have proposed tens of thousands of transcription factor-to-gene relationships for the cells. Miraldi and colleagues are currently testing their hypothetical models and developing preliminary evidence to support these. 

Miraldi sees the potential to break new scientific ground at Cincinnati Children’s, especially with opportunities for extensive collaboration and the blending of computational biology and immunology.

“There are dozens of labs doing cutting-edge experimental immunology, and they are eager to team up with a computational biologist to dig deeper with genomics datasets and build models,” she says. “Equally important are talented computational and mathematical biologists who want to combine forces to derive new modeling approaches as new data types and biotechnologies become available.”