Fengkai Zhang, M.D., M.Math.

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Computational Systems Biology Section
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A range of methodologies is utilized in systems biology to simulate biological pathways, uncovering the intricate ways molecules function within living organisms and enabling hypotheses about potential interactions and reaction rate ranges without relying on traditional experimental measurements. Among these, rule-based modeling abstracts similar reactions into rules that summarize the characteristics and binding states of individual molecular components within large molecular complexes. This approach offers a distinct advantage by simplifying the representation of molecular reactions, organizing entities within hierarchical structures that align with biological formats at both the molecular and sub-molecular levels, such as domains and binding sites. Furthermore, the rule-based modeling approach can address the combinatorial complexity inherent in biological systems.

Simmune is a software suite that uses rule-based modeling to simulate biological pathways. It constructs models of biological reactions through its unique icon-based modeling language from single reactions and provides a flexible, high-level network view for model inspection. Simmune can simulate models in well-stirred environments, efficiently explore large parameter spaces, and help users identify the most relevant parameters, offering insights into the relationships between different parameters. Additionally, Simmune supports the simulation of spatially resolved models in discrete grid morphologies and 3D environments, with the ability to analyze and visualize results at fine-grained subcellular levels.

Simmune supports the Multistate, Multicomponent, and Multicompartment Species Package for SBML Level 3 (SBML-Multi) to exchange rule-based models. This standard is part of an initiative by the COmputational Modeling in BIology NEtwork (COMBINE) community standards and formats for computational models. Our group leads the effort for the SBML-Multi standard.

Simmune is built with technologies including C/C++, Qt, VTK, CMake, Python, SQL, MongoDB, Boost, version control, Sundials, and parallel computing, providing a powerful, flexible modeling tool that remains user-friendly for biological researchers with limited computing expertise. Our group collaborates with researchers in immunology, proteomics, computational modeling, and systems biology.

Selected Publications

Xu X, Quan W, Zhang F, Jin T. A systems approach to investigate GPCR-mediated Ras signaling network in chemoattractant sensing. Mol Biol Cell. 2022 Mar 1;33(3):ar23.

Zhang F, Smith LP, Blinov ML, Faeder J, Hlavacek WS, Juan Tapia J, Keating SM, Rodriguez N, Dräger A, Harris LA, Finney A, Hu B, Hucka M, Meier-Schellersheim M. Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2. J Integr Bioinform. 2020 Jul 6;17(2-3):20200015.

Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, Bergmann FT, Finney A, Gillespie CS, Helikar T, Hoops S, Malik-Sheriff RS, Moodie SL, Moraru II, Myers CJ, Naldi A, Olivier BG, Sahle S, Schaff JC, Smith LP, Swat MJ, Thieffry D, Watanabe L, Wilkinson DJ, Blinov ML, Begley K, Faeder JR, Gómez HF, Hamm TM, Inagaki Y, Liebermeister W, Lister AL, Lucio D, Mjolsness E, Proctor CJ, Raman K, Rodriguez N, Shaffer CA, Shapiro BE, Stelling J, Swainston N, Tanimura N, Wagner J, Meier-Schellersheim M, Sauro HM, Palsson B, Bolouri H, Kitano H, Funahashi A, Hermjakob H, Doyle JC, Hucka M; SBML Level 3 Community members. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol. 2020 Aug;16(8):e9110.

Cheng HC, Angermann BR, Zhang F, Meier-Schellersheim M. NetworkViewer: visualizing biochemical reaction networks with embedded rendering of molecular interaction rules. BMC Syst Biol. 2014 Jun 16;8:70.

Zhang F, Angermann BR, Meier-Schellersheim M. The Simmune Modeler visual interface for creating signaling networks based on bi-molecular interactions. Bioinformatics. 2013 May 1;29(9):1229-30.

Angermann BR, Klauschen F, Garcia AD, Prustel T, Zhang F, Germain RN, Meier-Schellersheim M. Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat Methods. 2012 Jan 29;9(3):283-9.

Major Areas of Research
  • Design and development of systems biology simulation applications
  • Development of a standard of the Multistate, Multicomponent and Multicompartment Species Package for SBML Level 3 to exchange rule-based models
  • Simulating and analyzing well-stirred and spatially resolved computational models for signaling processes

Philip P. Adams, Ph.D.

Contact: philip.adams@nih.gov

Education:

Ph.D., 2017, Biomedical Sciences, University of Central Florida, FL
B.S., 2012, Biology, Summa Cum Laude, West Virginia Wesleyan College, WV

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Scientists Discover Cause, Potential Treatment for Cases of Deadly Autoimmune Disorder

NIAID Now |

NIAID-led scientists’ discovery of a hidden gene variant that causes some cases of a devastating inherited disease will enable earlier diagnosis of the disorder in people with the variant, facilitating earlier medical care that may prolong their lives. The researchers are working on a treatment for this unusual form of the rare autoimmune disease, known as APECED, and have traced its evolutionary origins. The findings are published in the journal Science Translational Medicine

APECED—short for autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy—causes multi-organ dysfunction, usually beginning in childhood, and can kill up to 30% of people with the syndrome. If diagnosed early and treated by a multidisciplinary healthcare team, however, people with APECED can survive into adulthood. Scientists in NIAID’s Laboratory of Clinical Immunology and Microbiology (LCIM) have developed a world-class APECED diagnostic and treatment program, currently caring for more than 100 patients as part of an observational study and serving as a resource for clinicians across the globe.

APECED is caused by mutations in a gene called AIRE, which provides instructions for making a protein that keeps the immune system’s T cells from attacking the body’s tissues and organs. These genetic mutations reduce or eliminate the protein’s normal function, leading to autoimmunity. 

Most people with APECED are diagnosed based on their clinical signs and symptoms as well as on genetic testing that confirms they have a disease-causing mutation in the AIRE gene. However, as the LCIM team studied people who came to NIH with APECED, they found 17 study participants with clinical signs and symptoms of the disease but no detectable mutations in AIRE. These participants shared two notable characteristics. The families of 15 of the 17 participants were wholly or partly from Puerto Rico, a relatively small, self-contained geographic area, suggesting that the individuals’ disease might have the same genetic cause. In addition, all 17 participants had the same harmless mutation to a single building block, or nucleotide, in both copies of their AIRE gene (one inherited from each parent). This suggested they all might have a similar stretch of genetic material in or around AIRE. These clues led the researchers to start hunting for a unique genetic mechanism that could be causing APECED in the group. 

The Quest for a Genetic Cause

Using technologies called whole-exome sequencing and whole-genome sequencing, the scientists determined the order of all the nucleotides in the DNA of each study participant. By examining and comparing these genetic sequences, the researchers discovered that the 17 participants had the same mutation to a single nucleotide located in a different part of the AIRE gene than the mutations commonly known to cause APECED. APECED-causing mutations usually occur in parts of the AIRE gene called “exons,” which contain the DNA code for the protein. The mutations also sometimes occur at either end of the large, non-coding sections of AIRE called “introns,” which are located in-between the exons. The newly discovered mutation was in the middle of an AIRE intron rather than at either end, so how it caused disease was initially unclear.

To solve this puzzle, the researchers examined what happens when the version of AIRE with this mid-intron mutation gets transcribed into mature messenger RNA (mRNA), the protein precursor. Normally, a molecule called a spliceosome detects the boundaries between introns and exons, cuts out the exons, and “pastes” them together in order. The scientists discovered that the mid-intron AIRE mutation fools the spliceosome into “thinking” that part of the intron is an exon, leading it to cut and paste part of the intron—extraneous genetic material—into the mature mRNA. This gives cells instructions to make an AIRE protein with an incorrect amino-acid sequence at one end. The researchers predicted and then showed that this protein can’t function normally, confirming that the mid-intron AIRE mutation causes APECED in the 17 study participants who previously lacked a genetic diagnosis. 

The scientists anticipate that the newly discovered AIRE variant will be added to genetic screening panels given to people who doctors suspect have APECED or who have a family history of the disease. This could facilitate earlier diagnosis and treatment of people with the mid-intron AIRE mutation, potentially prolonging their lives. It will also enable these individuals to receive genetic counseling to inform their family planning decisions. According to the researchers, the new findings also suggest that there may be other undiscovered, mid-intron mutations that cause APECED or other inherited diseases.

A Potential Treatment in the Making

Now NIAID LCIM scientists are working on a treatment for APECED caused by the mid-intron mutation. They engineered five different strings of nucleic acids, known as antisense oligonucleotides (ASOs), designed to hide the mutation from the spliceosome. Laboratory testing in cells with the mid-intron AIRE mutation showed that one ASO worked. Unable to “see” the mutation, the spliceosome cut out the correct AIRE exons and pasted them together to make mature mRNA that could be translated into a normal AIRE protein. Next, the researchers will test this mutation-masking tool in a mouse model of APECED with this specific mid-intron mutation. They expect results in two to three years. 

ASOs are an emerging form of treatment for rare genetic diseases, sometimes custom-made for just one person.

Origins of the Mutation

Through genetic and statistical analyses, the researchers estimated that the mid-intron mutation first occurred about 450 years ago. This timing coincides with when the first Europeans colonized Puerto Rico, hailing from the Cdiz province of Spain. Notably, one of the two study participants who did not have Puerto Rican ancestry also was from Cdiz and had the same set of DNA variants on one of his chromosomes as the participants with Puerto Rican ancestry. According to the researchers, these findings suggest that one or a few early Spanish colonizers of Puerto Rico carried the mid-intron AIRE mutation, and it eventually became a major cause of APECED in the Puerto Rican population. Further studies are needed to determine the prevalence of this cause of APECED among Puerto Ricans and other populations with Spanish ancestry.    

By contrast, one member of the study cohort had no known Puerto Rican or Spanish ancestry and did not share the same set of DNA variants as the other 16 participants. The investigators say this suggests that the mid-intron AIRE mutation also emerged independently in North America and will likely be found in additional Americans with APECED who do not have Puerto Rican or Spanish ancestry.

Note: APECED is also known as APS-1, short for autoimmune polyglandular syndrome type 1. 

References 

S Ochoa et al. A deep intronic splice-altering AIRE variant causes APECED syndrome through antisense oligonucleotide-targetable pseudoexon inclusion. Science Translational Medicine DOI: 10.1126/scitranslmed.adk0845 (2024).

D Karishma et al. Antisense oligonucleotides: an emerging area in drug discovery and development. Journal of Clinical Medicine DOI: 10.3390/jcm9062004 (2020).

F Collins. One little girl’s story highlights the promise of precision medicine. NIH Director’s Blog. https://directorsblog.nih.gov/tag/milasen/ Oct. 23, 2019. Accessed Oct. 30, 2024.

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NIAID Funds Cutting-Edge Genomics and Bioinformatics Programs

NIAID Now |

NIAID Funds Cutting-Edge Genomics and Bioinformatics Programs 

NIAID has announced six awards to continue the Genomics Centers for Infectious Diseases (GCIDs) and Bioinformatics Resource Centers (BRCs) for Infectious Diseases, both important data science networks offering critical resources for the scientific community. NIAID expects to commit approximately $19.1 million per year to fund the five-year programs. The awards mark the 20th anniversaries of the GCID and BRC programs and extend NIAID's history of investing in cutting-edge pathogen genomics and bioinformatics research – the relatively new field of using patient gene sequences and computer analysis to identify, predict and prevent disease. 

The GCIDs and BRCs provide public access to high-quality genomic data and data analytics technologies, tools, and training to facilitate discoveries by researchers studying viruses, bacteria, fungi, parasites, other eukaryotic pathogens, and vectors. In addition, in the event of an infectious disease outbreak, the GCID and BRC programs offer network expertise and resources and provide a coordinated research response.

For example, the GCIDs use innovative, large-scale genomics technology and bioinformatics tools to find specific genetic sequences to explain how pathogens cause disease and whether pathogens are resistant to available treatments. GCID studies can enhance understanding of infection mechanisms, track pathogen transmission dynamics, and improve detection – all leading to better diagnostics, prevention, treatment, and pathogen elimination strategies.

For more information, visit the GCID program website

The BRCs are publicly accessible online resources that include data on pathogens, vectors, and hosts. The newly funded BRCs will have four primary objectives: 

  1. To provide integrated data and bioinformatics resources for infectious diseases.
  2. To develop advanced innovative bioinformatics technologies, software, and tools to accelerate basic and applied human infectious diseases research.
  3. To offer state-of-the-art bioinformatics trainings, educational materials, and other community outreach activities for the infectious diseases research community in the United States and globally.
  4. To offer cutting-edge bioinformatics resources and analytics in response to emerging needs, outbreaks, and public health emergencies consistent with NIAID’s mission.

The newly funded BRCs will align with the NIH Strategic Plan for Data Science and incorporate globally distributed repositories and analytical capabilities that will be strengthened by a program-wide commitment to FAIR data principles and collaborative work. All three funded centers will conduct activities and advance research across all four programmatic objectives and will become operational soon after the awards are made. Two centers, the Bioinformatics Resource Analytics Center (BRC.analytics) and the Pathogen Data Network will address all pathogen types relevant to the NIAID mission and will continue to make available bioinformatics data compiled during previous funding periods from eukaryotic pathogens and vectors, and from bacteria and viruses. Both centers will have a specific focus on advancing the knowledge base and tools for bioinformatics analysis of eukaryotic genomes but will also advance technologies for bacterial and viral bioinformatics. The Bacterial and Viral Bioinformatics Resource Center (BV-BRC) will continue its focus on bacterial and viral pathogens, and bioinformatics data compiled for bacteria and viruses during previous funding periods will be found on its site.

Bioinformatics infrastructure advances anticipated include: providing uniform and easy access to numerous pathogen-relevant external resources; integrating infectious diseases data with additional human and clinical data; and providing large-scale automated bioinformatics workflows and dataset management.

The BRC program is expected to enhance NIAID’s outbreak and pandemic preparedness response by offering accessible platforms that integrate public health, pathogen and other data.  For more information, visit the BRC program website.

GCID award recipients are:

The Center for Advancing Genomic, Transcriptomic and Functional Approaches to Combat Globally Important and Emerging Pathogens

  • Principal Investigator/Director: Daniel Neafsey, Ph.D.
  • Institute: Broad Institute, Boston, Massachusetts

The Center for Integrated Genomics of Mucosal Infections

  • Principal Investigator/Director: Joseph Petrosino, Ph.D.
  • Institute: Baylor College of Medicine, Houston, Texas

The Michigan Infectious Disease Genomics (MIDGE) Center

  • Principal Investigator/Director: Adam Lauring, M.D., Ph.D.
  • Institute: The University of Michigan, Ann Arbor, Michigan

BRC award recipients are: 

The Bacterial and Viral Bioinformatics Resource Center (BV-BRC)  

  • Principal Investigator/Director: Rick Stevens, Ph.D.
  • Institute: University of Chicago, Chicago, Illinois
  • Website: https://www.bv-brc.org/

The Bioinformatics Resource Analytics Center (BRC.analytics)  

  • Principal Investigator/Director: Anton Nekrutenko, Ph.D.
  • Institute: Pennsylvania State University, University Park, Pennsylvania 
  • Website: https://brc-analytics.org/

The Pathogen Data Network 

  • Principal Investigator/Director: Aitana Neves, Ph.D.
  • Institute: Swiss Institute of Bioinformatics, Lausanne, Switzerland
  • Website: https://pathogendatanetwork.org/

 

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Thorsten Prüstel, Ph.D.

Section or Unit Name
Computational Systems Biology Section
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A major focus of our research is on developing computational modeling approaches that are not only able to predict the behavior of complex biological systems, such as the human immune system, but that also provide insights into the mechanisms that underlie the systems’ behavior. Such a mechanistic understanding of those cellular processes that play key roles in health and disease is critical in the search of novel treatment strategies. It requires simulation methods that can describe the intricate interactions of the systems’ components on a variety of biological scales, ranging from single molecules, over individual cells and multicellular structures to whole organisms.

On the most fundamental biological scale that our research covers, the level of single molecules and reaction-diffusion driven interactions of molecular complexes, subcellular processes behave in a stochastic fashion. Therefore, an important research objective has been the development of high-resolution single-particle stochastic simulation methods that can correctly and efficiently capture reaction-diffusion processes in the context of cellular biochemistry, for instance at and adjacent to cell surfaces, where many of the cellular signaling processes are initiated. Accordingly, a further major research goal has been the development of computational representations of arbitrarily shaped geometries. Here, a focus has been on models that are flexible enough to not only represent the shape of separate cells, but also interfaces between two cells. Such interfaces form, for instance, when two immune system cells exchange information on pathogens (see figure below).

The availability of efficient and detailed stochastic simulations is a prerequisite for establishing a bridge between the stochastic events of cellular biochemistry on the molecular scale and the higher-level cellular behavior that underlies health and disease.

A blue-colored model representing a single-particle stochastic simulation of a T-cell and an antigen-presenting cell interacting with each other via a T-cell protrusion contact.

Snapshot of a high-resolution single-particle stochastic simulation of a T-cell (blue-colored model) and an antigen-presenting cell interacting with each other via a T-cell protrusion contact. Bright dots represent single T-cell receptors and peptide: MHC complexes.

Selected Publications

Prüstel T, Meier-Schellersheim M. Space-time histories approach to fast stochastic simulation of bimolecular reactions. J Chem Phys. 2021 Apr 28;154(16):164111.

Johnson ME, Chen A, Faeder JR, Henning P, Moraru II, Meier-Schellersheim M, Murphy RF, Prüstel T, Theriot JA, Uhrmacher AM. Quantifying the roles of space and stochasticity in computer simulations for cell biology and cellular biochemistry. Mol Biol Cell. 2021 Jan 15;32(2):186-210.

Prüstel T, Meier-Schellersheim M. Unified path integral approach to theories of diffusion-influenced reactions. Phys Rev E. 2017 Aug;96(2-1):022151.

Prüstel T, Tachiya M. Reversible diffusion-influenced reactions of an isolated pair on some two dimensional surfaces. J Chem Phys. 2013 Nov 21;139(19):194103.

Prüstel T, Meier-Schellersheim M. Exact Green's function of the reversible diffusion-influenced reaction for an isolated pair in two dimensions. J Chem Phys. 2012 Aug 7;137(5):054104.

Angermann BR, Klauschen F, Garcia AD, Prüstel T, Zhang F, Germain RN, Meier-Schellersheim M. Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat Methods. 2012 Jan 29;9(3):283-9.

Visit PubMed for a complete publication listing.

Additional Information

Tools and Resources

Simmune Project

Major Areas of Research
  • Stochastic simulation approaches to cellular signaling
  • Computational models of cellular morphology 
  • Interplay between stochastic and spatial aspects of cellular signaling at cell-cell contacts (example: T-cell receptor activation)
  • Interactions between migrating cells and their environment

Comprehensive Analysis of RNAi-screen Data (CARD)

CARD is a comprehensive web-application for integrated analysis and interactive visualization of RNA interference (RNAi) screening data.

GCgx

Glucocorticoids are the cornerstone of anti-inflammatory and immunosuppressive therapy in humans. They are often the drugs of choice when rapid and potent control of an overactive immune system is necessary. This was exemplified recently by their successful use in the treatment of patients with severe COVID-19. Unfortunately, glucocorticoids also have serious side effects that affect every organ system, and each type of human cell has a very different response to glucocorticoids.

Iterative Bleaching Extends Multiplexity (IBEX) Knowledge-Base

The IBEX Imaging Community is an international group of scientists committed to sharing information on multiplexed imaging in a transparent and collaborative manner. This open, global repository is a central resource for reagents, protocols, panels, publications, software, and datasets.

NIAID Stats Calculator

The NIAID Stats Calculator application is provided by the Bioinformatics and Computational Biology Branch to allow users to perform quick sanity checks when exploring experimental results data.