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Suying Bao, PhD
Suying Bao, PhD

Suying Bao, a postdoctoral research scientist in the Chaolin Zhang lab , has been named an inaugural Precision Medicine Research fellow by Columbia’s Irving Institute of Clinical and Translational Research . The two-year fellowship aims to train postdocs to use genomics and complex clinical data to improve personalized and tailored clinical care and clinical outcomes. 

This fellowship “will provide me with more opportunities to translate my findings from basic science research into clinical application,” says Bao, “and pave my way towards an independent researcher in this field.” 

Bao’s expertise lies in RNA regulation at the interface of systems biology, ranging from the specificity of protein-RNA interaction to function of specific splice variants. RNA regulation is critical in proper cellular function; gaining deeper insights into this complex molecular mechanism will promote the development of precision medicine therapies. 

In this project, Bao is aiming to develop new approaches to identify causal noncoding regulatory variants (RVs) modulating post-transcriptional gene expression regulation, such as RNA splicing and stability.  “A majority of genetic variants associated with human diseases reside in noncoding genomic regions with regulatory roles,” notes Bao. “Thus, elucidating how these noncoding regulatory variants contribute to gene expression variation is a crucial step towards unraveling genotype-phenotype relationships and advancing precision medicine for common and complex diseases.”

To identify these RVs, she will leverage massive datasets of high-throughput profiles of gene expression and protein-RNA interactions generated from large cohorts of normal and disease human tissues and cell lines by multiple consortia, such as ENCODE, GTEx and CommonMind, and develop innovative computational methods of data mining. 

Hyundai $2.5M Grant to Columbia
Julia Glade Bender, MD, (center) at the Hyundai Hope on Wheels announcement Mar. 29 during the New York International Auto Show at the Jacob Javitz Center. (Photo courtesy of HHOW)

A team of researchers at Columbia University Irving Medical Center (CUIMC) has recently been awarded a five-year $2.5 million grant from Hyundai Hope on Wheels (HHOW) to fund innovative pediatric cancer research. 

The team at Columbia is being led by principal investigator (PI), Julia Glade Bender , MD, associate professor of pediatrics at CUIMC, with co-PIs Andrea Califano , Dr, chair of Columbia’s Department of Systems Biology and Darrell Yamashiro , MD, PhD, director of pediatric hematology, oncology and stem cell transplantation, along with researchers from Memorial Sloan Kettering, University of San Francisco Children’s and Dana-Farber Cancer Center. 

The Quantum Collaboration award from HHOW is aimed at funding research focused on childhood cancers with poor prognosis. At Columbia, the team will target osteosarcoma, the most commonly diagnosed bone tumor in children and adolescents. No new treatment approaches have successfully been introduced for osteosarcoma in nearly 40 years, and patients with the disease have not benefited from recent breakthroughs like immunotherapy or DNA sequencing and require a shift in the understanding and approach to therapy. 

Califano-Cancer Bottleneck
The N of 1 trial leverages systems biology techniques to analyze genomic information from a patient’s tumor. The goal is to identify key genes, called master regulators (green circles), which, while not mutated, are necessary for cancer cell survival. Master regulators are aberrantly activated by patient-specific mutations (X symbols) in driver genes (yellow circles), which are mutated in large cancer cohorts. Passenger mutations (blue circles) that are not upstream of master regulators have no effect on the tumor. (Image: Courtesy of the Califano Lab)

A novel N of 1 clinical trial led by the Califano Lab at Columbia University Irving Medical Center is focusing on rare or untreatable malignancies that have progressed on multiple lines of therapy, with the goal of identifying and providing more effective, customized therapies for patients. The approach is grounded in a computational platform developed over the last 14 years by the Califano Lab to allow accurate identification of a novel class of proteins that represent critical tumor vulnerabilities and of the drug or drug combination that can most effectively disarm these proteins, thus killing the tumor. Platform predictions are then validated in direct tumor transplants in mice, also known as Patient-Derived Xenografts (PDX). 

“We call these proteins master regulators and have developed innovative methodologies that allow their discovery on an individual patient basis,” said Dr. Andrea Califano , Clyde and Helen Wu Professor of Chemical and Systems Biology and chair of the Department of Systems Biology at Columbia. 

Project GENIE
Richard Carvajal (left) and Raul Rabadan are lead PIs on Project GENIE

Columbia University Irving Medical Center (CUIMC) has recently joined 11 new institutions to collaborate on Project GENIE, an ambitious consortium organized by the  American Association for Cancer Research . An international cancer registry built through data sharing,  Project GENIE , which stands for Genomics Evidence Neoplasia Information Exchange, brings together leading institutions in cancer research and treatment in order to provide the statistical power needed to improve clinical decision-making, particularly in the case of rare cancers and rare genetic variants in common cancers. Additionally, the registry, established in 2016, is powering novel clinical and translational research. In its first two years, Project GENIE has been able to accumulate and make public more than 39,000 cancer genomic records, de-identified to maintain patient privacy. 

Judith in the lab
Judith Kribelbauer

As a child growing up in a small town in Germany, Judith Kribelbauer excelled in science, counting chemistry and mathematics as her two favorite subjects from grade school through high school. After high school graduation, she attended the Ruprecht-Karls University in Heidelberg to pursue a bachelor’s degree in chemistry, which she completed in 2012. 

Becoming more serious about pursuing scientific research, Kribelbauer, who is graduating this May with a PhD in the Systems Biology Integrated Program, moved to the U.S. to work as a graduate exchange student at the University of North Carolina-Chapel Hill (UNC) before enrolling at Columbia University in 2013. At UNC, using SHAPE-MaP sequencing technology, she researched the structural basis of the HIV-1 RNA frame-shift element, a sequence that causes ribosomes to shift reading frames, therefore producing truncated proteins.  

Columbia’s collaborative environment—the chance to work with researchers spanning areas from biology to chemistry and physics to computer science—is what drew her to the University and ultimately to concentrating in systems biology. 

“Thanks to this unique environment, I could realize my dream research project—combining both experimental and computational approaches,” says Kribelbauer. “This comprehensive training allowed me to conduct my thesis research in two labs, with both PhD advisers having appointments in Systems Biology.”

May 7, 2018

From Code to Cure

Columbia Magazine

Published Spring 2018 cover story , Columbia Magazine

As reported by David J. Craig, senior editor at Columbia Magazine , we are living in the age of big data, and with every link we click, every message we send, and every movement we make, we generate torrents of information. In the past two years, the world has produced more than 90 percent of all the digital data that has ever been created. New technologies churn out an estimated 2.5 quintillion bytes per day. 

Today, researchers at Columbia University Irving Medical Center (CUIMC) are using the power of data to identify previously unrecognized drug side effects; they are predicting outbreaks of infectious diseases by monitoring Google search queries and social-media activity; and they are developing novel cancer treatments by using predictive analytics to model the internal dynamics of diseased cells. These ambitious projects, many of which involve large interdisciplinary teams of computer scientists, engineers, statisticians, and physicians, represent the future of academic research.

Craig covers Dr. Nicholas Tatonetti's work involving prescription drug safety and his innovative use of digital health and clinical records and Dr. Andrea Califano's unconventional computational approaches in advancing cancer research.

To read the full article , visit the online issue of Columbia Magazine

Raul Rabadan
Raul Rabadan

Systems Biology Professor Raul Rabadan, Phd , has been awarded a Philip A. Sharp Innovation in Collaboration award from Stand Up to Cancer (SU2C) , a group established by film and media leaders to fund cancer research projects that have the potential to quickly deliver new therapies to patients. Dr. Rabadan has received the award jointly with collaborator Dan A. Landau, MD, PhD, of Weill Cornell Medicine.

A theoretical physicist whose expertise lies in the cross section of mathematical genomics, tumor evolution, and cancer research, Dr. Rabadan will work together with Dr. Landau on their winning project, “Cupid-seq—high throughput transcriptomic spatial mapping of immune-tumor interactions in the micro-environment.”  The investigators will devise a novel sequencing technique and computational method for better understanding immune recognition mechanism in glioblastoma. Dr. Rabadan is currently a principal investigator on the SU2C-National Science Foundation Drug Combination Convergence Team and Dr. Lau is a 2016 recipient of a SU2C Innovative Research Grant.

The idea behind the Sharp Awards is to fund projects that involved SU2C researchers who have not yet worked together, including collaborations between members of Dream Teams, Research Teams, and Convergence teams, and between SU2C members and recipients of its innovative research grants. The latter are typically early-career investigators. The Sharp Awards are named after Sharp, Nobel laureate and institute professor at MIT and the Koch Institute for Integrated Cancer Research, to honor the emphasis he has placed on the importance of collaborative research. 

Dr. Rabadan, who also is professor of biomedical informatics at Columbia University, is one of 10 recipients of this year’s Sharp Awards; investigators hail from the U.S., Canada, and the Netherlands, and projects are being funded from a pool of $1.25 million. 

Andrea Califano
Andrea Califano, Dr, chair of Columbia's Department of Systems Biology

The Chan Zuckerberg Initiative (CZI) has awarded Andrea Califano, Dr, a new grant in support of his work to develop a comprehensive library of regulatory interactions within molecularly defined cellular populations and molecular determinants (master regulators) of individual cells’ state. This will arm scientists with a unique resource to study biology at the individual cell level and to gain further insight into the fundamental understanding of molecularly distinct cell types.

With the support of CZI, founded by Facebook CEO Mark Zuckerberg and his wife, Priscilla Chan, Dr. Califano, chair of Columbia’s Department of Systems Biology, and his group will apply their computational methods that accurately and systematically measure and analyze regulatory interaction at the single cell level to elucidate distinct cellular states and to establish both cell-state markers, as well as the proteins that are causally responsible for implementing that state. 

A critical advantage offered by the approaches that the Califano Lab brings to the CZI community is that of addressing one of the most critical issues in single cell biology characterization. Specifically, since the depth of transcriptional sequencing of single cells is generally a very small fraction of what can be achieved from bulk tissue, most of the genes are actually lost and only the 20% to 30% of the genes that are most highly expressed can be detected. This has been dubbed the gene dropout problem. By using the recently published metaVIPER algorithm, this problem is eliminated as regulatory networks are used to accurately infer the activity of 6,000 proteins that include all the critical players in cell state implementation and modulation, even if their RNA cannot be detected, thus fully addressing gene dropout and allowing much deeper investigation of single cell biology. 

Coauthors
Study lead coauthors Nathan Johns (left), systems biology graduate student in the Wang Lab, and Antonio Gomes, former member of the Wang Lab, now at Memorial Sloan Kettering Cancer Center.

Advances in synthetic biology have already spurred innovation in the areas of drug development, chemical production and health diagnostics. To help push the field even further, and potentially at a more rapid pace, a new, comprehensive resource devised by Columbia University investigators will help synthetic biologists better engineer designs for complex biological systems.

A team of researchers, led by Harris Wang, PhD, assistant professor of systems biology and of pathology and cell biology, report the characterization and analysis of thousands of bacterial regulatory elements in different species of bacteria. The paper , published March 19, appears in Nature Methods .

Synthetic biology employs well-characterized genetic parts to assemble gene circuits with specific functions, such as producing chemicals or sensing the environment. The toolbox of genetic parts to make functioning genetic circuits, however, has been limiting. A key shortfall is the availability of precisely measured regulatory sequences-segments of DNA responsible for dialing up or dialing down the expression of proteins within an organism. For many commercially useful bacteria, tuning gene expression has been challenging because of a lack of reliable regulatory sequences. 

"Synthetic biology is now at a precipice where we are not just demonstrating proof-of-concept in the laboratory but we're moving toward real-world applications," says Nathan Johns , lead coauthor of the paper, a member of the Wang Lab and a graduate student in the Department of Systems Biology at Columbia. "To facilitate this, having a wide array of useful genetic components and measurement techniques-in our case, regulatory sequences-are extremely helpful." 

Feb 7-8 Cancer Genomics Symposium

Pictured above, Adolfo Ferrando (left), professor of pediatrics and of pathology and cell biology at Columbia, with Luis Arnes, associate research scientist and first-place winner of the symposium's poster competition; For photos from the symposium, visit the gallery page . Credit: Lydia Lee Photography

A multidisciplinary team of researchers across Columbia University have been busy addressing the complex challenges in basic and translational cancer research. Faculty and investigators are bridging their expertise in fields ranging from mathematics, biology, and engineering to physics, genomics, and chemistry to develop innovative approaches to better understand, for instance, cancer disease progression, drug resistance, and the systems-wide network of tumor evolution.

Central to this ongoing work is research grounded in cancer genomics and mathematical data analysis explored during a two-day conference Feb. 7-8 co-hosted by the National Cancer Institute (NCI) centers at Columbia University Medical Center, Cornell University, and Memorial Sloan Kettering Cancer Center. (Visit the Rabadan Lab YouTube Channel for video of the symposium).

"Genomics is becoming an important tool for the quantitative study of biological systems,” says Raul Rabadan, PhD , professor of systems biology at Columbia and director of the Center for Topology of Cancer Evolution and Heterogeneity and of the Program for Mathematical Genomics . “This meeting organized by four different NCI centers addressed some of the important challenges and new perspectives on the quantitative understanding of cancer using genomics tools.”

Two new precision medicine tests, born out of research from the Califano Lab, that look beyond cancer genes to identify novel therapeutic targets have just received New York State Department of Health approval and are now available to both oncologists and cancer researchers for use at the front lines of patient care. As reported by Columbia University Irving Medical Center (CUIMC), the tests are based on research conducted by CUIMC investigators—and could pave the way for a more precise approach to cancer therapy and help find effective drugs when conventional approaches to precision medicine have failed.

“This means that the vast majority of cancer patients who do not have actionable mutations, or have not responded to, or have relapsed after chemotherapy or targeted therapy, now have access to additional tests that can help their oncologist select the treatment best suited to their specific tumor,” says the tests’ lead developer, Andrea Califano, Dr., chair of systems biology at Columbia University Vagelos College of Physicians and Surgeons.

The two tests, DarwinOncoTreat and DarwinOncoTarget, are available exclusively through the Laboratory of Personalized Genomic Medicine in the Department of Pathology and Cell Biology at Columbia University Vagelos College of Physicians and Surgeons. The tests were developed by DarwinHealth, a Manhattan-based biotech firm founded in 2015 by Dr. Califano and colleague, Gideon Bosker, MD.

For the complete article, visit the CUIMC Newsroom.

Courtesy of The Olive Lab

Shown here, a human pancreatic tumor stained with Masson's trichrome; Image credit: Dr. Kenneth Olive

The Lustgarten Foundation has awarded Columbia University’s Herbert Irving Comprehensive Cancer Center (HICCC) a three-year grant, as part of its Translational Clinical Program, to test a new precision medicine approach to the treatment of metastatic pancreatic cancer.

“The prevailing model in personalized cancer treatment is to attack the DNA mutations that are believed to be driving an individual patient’s tumor,” says principal investigator Kenneth P. Olive, PhD , assistant professor of medicine and pathology & cell biology at HICCC. “While this approach has been astonishingly effective for a handful of rare cancers, we expect it will only work for a very small fraction of patients with the most common types of cancer.”

Pancreatic ductal carcinoma (PDA)—the most common form of pancreatic cancer—is a case in point. Researchers have identified few genetic drivers in pancreatic tumors, and the most common driver ( KRAS ) is not easily targeted. Conservatively, only about 15 percent of PDA patients are likely to benefit from conventional DNA mutation-based precision medicine therapies and most of these will either not respond or will relapse with a drug-resistant form of the disease.

“Our study takes an entirely new approach,” says Dr. Olive. “Instead of looking at the mutations encoded in a tumor’s DNA, we analyze the tumor’s RNA. Since RNA is the tissue-specific ‘working copy’ of a cancer cell’s DNA, it’s a more accurate reflection of the genetic programs that are active in a tumor and critical for its survival. We can then match the patient to approved and investigational drugs that inhibit those programs.”

Nicholas P. Tatonetti, PhD, has recently been named director of clinical informatics at the Institute for Genomic Medicine (IGM) at Columbia University Medical Center. In this new role, he is charged with planning, organizing, directing and evaluating all clinical informatics efforts across the Institute. In particular, he will focus on the integration of electronic health record data for use in genetics and genomics studies.

Dr. Tatonetti, who is Herbert Irving Assistant Professor of Biomedical informatics with an interdisciplinary appointment in the Department of Systems Biology, specializes in advancing the application of data science in biology and health science. Researchers in his lab integrate their medical observations with systems and chemical biology models to not only explain drug effects, but also further understanding of basic biology and human disease. They focus also on integration of high throughput data capture technologies, such as next-generation genome and transcriptome sequencing, metabolomics, and proteomics, with the electronic medical record to study the complex interplay between genetics, environment, and disease.

At the Institute for Genomic Medicine, researchers are focused on innovative approaches to genomic medicine. Their multi-tiered approach to genomic medicine utilizes large scale genomic sequencing and analysis, paired with functional biology to advance the diagnosis, characterization, and treatment of genetic diseases. IGM is playing a critical role in Columbia’s overall Precision Medicine Initiative, a major University-wide effort to provide medical diagnosis, prevention and treatment based on an individual’s variation in genes, environment, and lifestyle. 

Dr. Tatonetti, who joined Columbia in 2012, is also affiliated with the Center for Computational Biology and Bioinformatics, the Department of Medicine, the Department of Biomedical Informatics, and the Center for Cancer Systems Therapeutics.

Broad, Columbia collaborators
Three of the investigators in new Columbia, Broad Institute research collaboration aimed at gastric and esophageal cancer; L to R: Dr. Andrea Califano, Dr. Cory Johannessen, and Dr. Adam Bass (Johannessen image: Martin Adolfsson; Bass image: Sam Ogden/Dana-Farber Cancer Institute)

A research collaboration underway between Columbia’s Department of Systems Biology, the Broad Institute of MIT and Harvard, and Columbia University Medical Center (CUMC) is working to accelerate the discovery of new cancer drug combinations targeted at gastric and esophageal cancer. These tumors have not yet attracted prominent research focus and attention, and yet the general outcome for patients with these diseases is poor. According to the American Cancer Society, survival rates are only 20% at five years after diagnosis.

The newly formed research alliance between research teams at Columbia and at the Broad Institute came about thanks to a four-year gift by the Price Family Foundation, known for its philanthropic support of education, health, and biomedical research.

The Columbia-Broad team includes Dr. Andrea Califano , cofounder and chair of the Department of Systems Biology; Dr. Adam Bass , associate member of the Broad Institute; Dr. Cory Johannessen, senior research scientist at the Broad Institute; Dr. Josh Sonnett , the director of The Price Family Center for Comprehensive Chest Care, Lung and Esophageal Center at Columbia; and Dr. Naiyer A. Rizvi , the Price Chair in Clinical Translational Research at Columbia.

In 2016, the Price Family Foundation suggested that a team of scientists at the Broad Institute meet with researchers from CUMC. At the time, the Foundation was eager to leverage the project at the Broad—where researchers had uncovered an interesting finding for gastric and esophageal cancer—with innovative cancer systems biology work it was supporting at CUMC, focusing on the same diseases.

HTS
Research scientist Hai Li holds up a 384-well plate, pictured in front of Columbia Genome Center's Hamilton Star automation system for HTS; Image credit: Systems Biology

Drug screening and analysis is critical in advancing research and discovery of cancer therapeutics. To this end, a Systems Biology-led team of investigators has recently developed PLATE-Seq, a new technique for low-cost, bulk mRNA sequencing. Coupled with genome-wide regulatory network analysis, the novel PLATE-Seq method advances the goal of providing cancer patients with personalized treatment.

Developed by the labs of Peter Sims and Andrea Califano , PLATE-Seq stands for “pooled library amplification for transcriptome expression” sequencing, and enables genome-wide mRNA profiling specifically designed to complement high-throughput screening assays. High-throughput screening, or HTS, represents a key component of drug discovery and technology used widely in biomedical research. Due to cost or complexity, most screens are still performed using low-complexity reporters, such as cell viability, protein-protein interactions and cell growth, for example, but there is a growing need to couple this screening protocol with genome-wide reporters, to measure the activity of many proteins across the genome.

“Our PLATE-Seq method helps us generate a more comprehensive portrait of drug activity,” said Dr. Sims, assistant professor of systems biology, known for his innovative work in single-cell RNA sequencing. “We’ve been able to show that our technique cuts the cost for gene expression profiling considerably, by incorporating a method we devised for ‘barcoding’ cDNA samples and combining this with computational methods from the Califano Lab that are highly effective on low-coverage sequencing data. This method allows us to sequence 96 samples per plate and 768 samples per sequencing run.”

On average, PLATE-Seq reduces the cost of genome-wide screening from around $400 per sample down to approximately $25 per sample. Genome-wide sequencing is important in advancing our understanding and prediction of disease and in identifying potential treatments.

Topology data analysis of cancer samples

Shown here, topology data analysis of cancer samples; Image credit: The Rabadan Lab

The new Program for Mathematical Genomics (PMG) is aiming to address a growing—and much-needed—area of research. Launched in the fall of 2017 by Raul Rabadan , a theoretical physicist in the Department of Systems Biology, the new program will serve as a research hub at Columbia University where computer scientists, mathematicians, evolutionary biologists and physicists can come together to uncover new quantitative techniques to tackle fundamental biomedical problems.

"Genomic approaches are changing our understanding of many biological processes, including many diseases, such as cancer," said Dr. Rabadan, professor of systems biology and of biomedical informatics. "To uncover the complexity behind genomic data, we need quantitative approaches, including data science techniques, mathematical modeling, statistical techniques, among many others, that can extract meaningful information in a systematic way from large-scale biological systems." 

This new program is being built upon collaborative research opportunities to explore and develop mathematical techniques for biomedical research, leading to a deeper understanding of areas such as disease evolution, drug resistance and innovative therapies. Inaugural members of the new program include faculty across several disciplines: statistics, computer science, engineering and pathology, to name a few. The program also will provide education and outreach to support and promote members' work, including joint discussion groups, the development of cross-campus courses and scientific meetings. 

In honor of its launch, PMG will co-host a two-day symposium February 7 to 8 on cancer genomics and mathematical data analysis. Guest speakers from Columbia University, Memorial Sloan Kettering and Cornell University will present a comprehensive overview of quantitative methods for the study of cancer through genomic approaches. 

Regulators of mesenchymal GBM subtype

An example of tumor oncotecture. Transcription factors involved in the activation of mesenchymal glioblastoma subtype are shown in purple. Together, they comprise a tightly knit tumor checkpoint, controlling 74% of the genes in the mesenchymal signature of high-grade glioma. CEBP (both β and δ subunits) and STAT3 regulate the other three transcription factors in the tumour checkpoint, synergistically regulating the state of mesenchymal GBM cells. (Image: Nature Reviews Cancer)

In a detailed Perspective article published in Nature Reviews Cancer, Department of Systems Biology chair Andrea Califano and research scientist Mariano Alvarez (DarwinHealth) summarize more than a decade of work to propose the existence of a universal, tumor independent “oncotecture” that consistently defines cancer at the molecular level. Their findings, they argue, indicate that identifying and targeting highly conserved, essential proteins called master regulators — instead of the widely diverse genetic and epigenetic alterations that initiate cancer and have been the focus of much cancer research — could offer an effective way to classify and treat disease.

As coverage of the paper in The Economist reports:

ONE of the most important medical insights of recent decades is that cancers are triggered by genetic mutations. Cashing that insight in clinically, to improve treatments, has, however, been hard. A recent study of 2,600 patients at the M.D. Anderson Cancer Centre in Houston, Texas, showed that genetic analysis permitted only 6.4% of those suffering to be paired with a drug aimed specifically at the mutation deemed responsible. The reason is that there are only a few common cancer-triggering mutations, and drugs to deal with them. Other triggering mutations are numerous, but rare—so rare that no treatment is known nor, given the economics of drug discovery, is one likely to be sought. 

Facts such as these have led many cancer biologists to question how useful the gene-led approach to understanding and treating cancer actually is. And some have gone further than mere questioning. One such is Andrea Califano of Columbia University, in New York. He observes that, regardless of the triggering mutation, the pattern of gene expression—and associated protein activity—that sustains a tumour is, for a given type of cancer, almost identical from patient to patient. That insight provides the starting-point for a different approach to looking for targets for drug development. In principle, it should be simpler to interfere with the small number of proteins that direct a cancer cell’s behaviour than with the myriad ways in which that cancer can be triggered in the first place. (Read full article.)

PrePPI inputs
PrePPI predicts the likelihood that two proteins A and B are capable of interacting based on their similarities to other proteins that are known to interact. This requires integrating structural data (green) as well as other kinds of information (blue), such as evidence of protein co-activity in other species as well as involvement in similar cellular functions. PrePPI now offers a searchable database of unprecedented scope, constituting a virtual interactome of all proteins in human cells. (Image courtesy of eLife.) 

The molecular machinery within every living cell includes enormous numbers of components functioning at many different levels. Features like genome sequence, gene expression, proteomic profiles, and chromatin state are all critical in this complex system, but studying a single level is often not enough to explain why cells behave the way they do. For this reason, systems biology strives to integrate different types of data, developing holistic models that more comprehensively describe networks of interactions that give rise to biological traits. 

Although the concept of an interaction network can seem abstract, at its foundation each interaction is a physical event that takes place when two proteins encounter one another, bind, and cause a change that affects a cell’s activity. In order for this to take place, however, they need to have compatible shapes and physical properties. Being able to predict the entire universe of possible pairwise protein-protein interactions could therefore be immensely valuable to systems biology, as it could both offer a framework for interpreting the feasibility of interactions proposed by other methods and potentially reveal unique features of networks that other approaches might miss. 

In a 2012 paper in Nature, scientists in the laboratory of Barry Honig first presented a landmark algorithm and database they call PrePPI (Predicting Protein-Protein Interactions). At the time, PrePPI used a novel computational strategy that deploys concepts from structural biology to predict approximately 300,000 protein-protein interactions, a dramatic increase in the number of available interactions when compared with experimentally generated resources.

Since then, the Honig Lab has been working hard to improve PrePPI’s scope and usefulness. In a paper recently published in eLife they now report on some impressive developments. With enhancements to their algorithm and the incorporation several new types of data into its analysis, the PrePPI database now contains more than 1.35 million predictions of protein-protein interactions, covering about 85% of the entire human proteome. This makes it the largest resource of its kind. In parallel with these improvements, the investigators have also begun to apply PrePPI in new ways, using the information it contains to provide new kinds of insights into the organization and function of protein interaction networks.

Cell Types in Autism

By inventing a new computational pipeline called DAMAGES, Chaolin Zhang and Yufeng Shen showed that brain cell types on the left of the plot are more prone to have rare autism risk mutations than cell types at the right. Narrowing the focus to these types of cells also helped to identify a molecular signature of the disorder that involves haploinsufficiency. Figure: Human Mutation.

Autism, a spectrum of neurodevelopmental disorders typically identified during early childhood, is widely thought to be the result of genetic alterations that change how the growing brain is wired. Nevertheless, despite a substantial effort in the field of autism genetics, the specific alterations that place one child at greater risk than another remain elusive. Although the list of alterations associated with autism is growing, it has been difficult to conclusively distinguish those that truly increase disease risk from those that are merely coincident with it. One troubling reason for this is that research so far seems to indicate that specific genetic abnormalities associated with autism risk are extremely rare, with many being found only in single patients. This has made it hard to reproduce findings conclusively.

In a paper recently published in the journal Human Mutation, Department of Systems Biology faculty members Chaolin Zhang and Yufeng Shen describe a method and some new findings that could help to more precisely identify rare autism-driving alterations. A new analytical pipeline they call DAMAGES (Disease Associated Mutation Analysis using Gene Expression Signatures) uses a unique approach to identifying autism risk genes, looking at differences in gene expression among different cell types in the brain in order to focus more specifically on mechanisms that are likely to be relevant for autism. Using this approach, they identified a pronounced molecular signature that is shared by disease risk genes due to haploinsufficiency, a type of genetic alteration that causes a dramatic drop in the expression of a particular protein.

September 28, 2016

Can Math Crack Cancer's Code?

An essay coauthored by Andrea Califano (Chair, Department of Systems Biology) and Gideon Bosker and published in the Wall Street Journal asks whether quantitative modeling could reveal the keys for turning cancer off. They write:

  • Disappointed with the slow pace of discovery and inclined to look for elegant, universal explanations for nature’s conundrums, many cancer researchers have increasingly been asking: Is there some sort of “Da Vinci Code” for cancer? And can we crack it using mathematics?

    Quantitative modeling has been extremely successful in disciplines as diverse as astronomy, physics, economics and computer science. Can “cancer quants”—scientists applying quantitative analyses to the landscape of cancer biology—find the answers we seek? And, if so, what would the new paradigm look like? 

The essay goes on to describe how computational methods developed in the Califano Lab are being tested in personalized N of 1 clinical trials to identify essential checkpoints in the molecular regulatory networks that sustain individual patients' tumors — as well as drugs capable of targeting them.

Click here to read the essay. (subscription may be required)

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