Reinhard Laubenbacher

Reinhard Laubenbacher

Director

Department: MD-PULMONARY SYSTEMS MEDICINE
Business Phone: (352) 294-2350

About Reinhard Laubenbacher

Dr. Laubenbacher joined the University of Florida in May 2020 as a professor in the Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine. He is the director of the Laboratory for Systems Medicine. Prior to joining UF, he served as director of the Center for Quantitative Medicine and Professor in the Department of Cell Biology in the University of Connecticut School of Medicine. Concurrently, he held an appointment as Professor of Computational Biology at the Jackson Laboratory for Genomic Medicine. He is a fellow of AAAS, the Society for Mathematical Biology, and the American Mathematical Society. Since 2016, he serves as editor-in-chief of the Bulletin of Mathematical Biology, the flagship journal of the Society for Mathematical Biology. Dr. Laubenbacher is a mathematician by training, and his broad research interests lie in computational and mathematical systems biology, with applications to human health. Most of his research is in collaboration with a broad spectrum of scientists and clinicians.

Accomplishments

Fellow
2017 · Society for Mathematical Biology
Fellow
2015 · American Association for the Advancement of Science
Fellow
2012 · American Mathematical Society

Research Profile

The Laubenbacher Lab is part of the Laboratory for Systems Medicine. The overarching focus is the development and application of mathematical and computational technology for the improvement of human health. Interests include multi-scale modeling and control of disease processes, systems biology, and computational immunology. We are highly collaborative, maintaining and seeking partnerships with clinical, basic science, computational, and private sector labs and entities. Our expertise includes the development of mechanistic and data-driven multi-scale models of disease-relevant processes, model-driven control and optimization problems, and cutting-edge data science methods applied to high-dimensional data from the molecular to the patient scale. Currently funded projects include the following.

1. Multiscale modeling of the battle over iron in invasive lung infection (NIH 1 R011AI1351128-01). Invasive aspergillosis is among the most common fungal infection in immunocompromised hosts and carries a poor outcome. Current therapeutic approaches have been focused primarily on the pathogen, but a better understanding of the components of host defense in this infection may lead to the development of new treatments against this infection, possibly in combination with antifungal drugs. Iron is essential to all living organisms, and restricting iron availability is a critical mechanism of antimicrobial host defense against many microorganisms. This mechanism has the potential to be harnessed therapeutically, for example with drugs that enhance the host’s iron sequestration mechanisms. The overarching goal of this project is to develop a multi-scale mathematical model that can serve as a simulation tool of the role of iron in invasive aspergillosis.

2. Modular design of multiscale models, with an application to the innate immune response to fungal respiratory pathogens. (1U01EB024501-01, NSF CBET-1750183) Increased availability of biomedical data sets across spatial and temporal scales makes it possible to calibrate complex models that capture integrated processes from the molecular to the whole organism level. This complexity poses multiple challenges related to mathematical modeling, software design, validation, reproducibility, and extensibility. Visualization of model features and dynamics is a key factor in the usability of models by domain experts, such as experimental biologists and clinicians. This project addresses these challenges in the context of the immune response to an important respiratory fungal infection. Its goal is to develop a novel modular approach to model architecture. The overarching computational goal is to develop a novel approach to the modular design of multiscale models.

3. Control of heterogeneous microbial communities using model-based multi-objective optimization (NIH 1R01GM127909-01, NIH 3 R01 GM127909-01S1) The project addresses an important biomedical problem: how to control biofilms formed by Candida albicans, a dimorphic fungus that is an important cause of both topical and systemic fungal infection in humans, in particular immunocompromised patients. C. albicans biofilms also form on the surface of implantable medical devices, and are a major cause of nosocomial infections. In recent years, it has been recognized that interactions with bacterial species integrated into biofilms can affect C. albicans virulence and other properties, It is therefore important to understand the interactions of C. albicans with bacterial species, in particular metabolic interactions. The next step then is to understand and, ultimately, control how varying compositions of the different microbial species affect their metabolic state and their ability to form biofilms. This project approaches the problem through model-based design of optimal compositions of the bacterial species for control of fungal growth, accomplished through a combination of the construction of a novel computational model of a heterogeneous biofilm consisting of bacterial as well as fungal species, and novel mathematical tools for dimension reduction and optimization. The applicability of the results of this project extends far beyond biofilms, such as studies of the human microbiome.

Open Researcher and Contributor ID (ORCID)

0000-0002-9143-9451

Areas of Interest
  • Disease Modeling
  • Mechanism-based mathematical modeling
  • Systems biology
  • data science

Publications

2020
Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach.
International journal of environmental research and public health. 17(19) [DOI] 10.3390/ijerph17197271. [PMID] 33027932.
2020
The contribution of microRNA-mediated regulation to short- and long-term gene expression predictability.
Journal of theoretical biology. 486 [DOI] 10.1016/j.jtbi.2019.110055. [PMID] 31647935.
2020
Sensitivity of comorbidity network analysis.
JAMIA open. 3(1):94-103 [DOI] 10.1093/jamiaopen/ooz067. [PMID] 32607491.
2020
Systems biology of ferroptosis: A modeling approach.
Journal of theoretical biology. 493 [DOI] 10.1016/j.jtbi.2020.110222. [PMID] 32114023.
2019
A mathematical model of combined CD8 T cell costimulation by 4-1BB (CD137) and OX40 (CD134) receptors.
Scientific reports. 9(1) [DOI] 10.1038/s41598-019-47333-y. [PMID] 31350431.
2019
Connecting the molecular function of microRNAs to cell differentiation dynamics.
Journal of the Royal Society, Interface. 16(158) [DOI] 10.1098/rsif.2019.0437. [PMID] 31551049.
2019
Control of Intracellular Molecular Networks Using Algebraic Methods.
Bulletin of mathematical biology. 82(1) [DOI] 10.1007/s11538-019-00679-w. [PMID] 31919596.
2019
Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors.
PLoS computational biology. 15(6) [DOI] 10.1371/journal.pcbi.1007026. [PMID] 31194735.
2019
PlantSimLab – a modeling and simulation web tool for plant biologists.
BMC bioinformatics. 20(1) [DOI] 10.1186/s12859-019-3094-9. [PMID] 31638901.
2019
Topological Data Analysis.
Bulletin of mathematical biology. 81(7) [DOI] 10.1007/s11538-019-00610-3. [PMID] 31066000.
2018
A Systems Biology Approach to Understanding the Pathophysiology of High-Grade Serous Ovarian Cancer: Focus on Iron and Fatty Acid Metabolism.
Omics : a journal of integrative biology. 22(7):502-513 [DOI] 10.1089/omi.2018.0060. [PMID] 30004845.
2018
An important role for periplasmic storage in Pseudomonas aeruginosa copper homeostasis revealed by a combined experimental and computational modeling study.
Molecular microbiology. 110(3):357-369 [DOI] 10.1111/mmi.14086. [PMID] 30047562.
2018
Applications of network analysis to routinely collected health care data: a systematic review.
Journal of the American Medical Informatics Association : JAMIA. 25(2):210-221 [DOI] 10.1093/jamia/ocx052. [PMID] 29025116.
2018
Editorial.
Bulletin of mathematical biology. 80(12):3069-3070 [DOI] 10.1007/s11538-018-0501-8. [PMID] 30171473.
2018
The innate immune response to ischemic injury: a multiscale modeling perspective.
BMC systems biology. 12(1) [DOI] 10.1186/s12918-018-0580-z. [PMID] 29631571.
2017
Optimization and Control of Agent-Based Models in Biology: A Perspective.
Bulletin of mathematical biology. 79(1):63-87 [DOI] 10.1007/s11538-016-0225-6. [PMID] 27826879.
2017
Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine.
PloS one. 12(3) [DOI] 10.1371/journal.pone.0173444. [PMID] 28329003.
2017
Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.
Journal of the Royal Society, Interface. 14(131) [DOI] 10.1098/rsif.2017.0150. [PMID] 28659410.
2017
Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective.
PLoS computational biology. 13(2) [DOI] 10.1371/journal.pcbi.1005352. [PMID] 28166223.
2016
A computational model of invasive aspergillosis in the lung and the role of iron.
BMC systems biology. 10 [DOI] 10.1186/s12918-016-0275-2. [PMID] 27098278.
2016
AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms.
Bioinformatics (Oxford, England). 32(15):2396-8 [DOI] 10.1093/bioinformatics/btw120. [PMID] 27153722.
2016
Costimulation Endows Immunotherapeutic CD8 T Cells with IL-36 Responsiveness during Aerobic Glycolysis.
Journal of immunology (Baltimore, Md. : 1950). 196(1):124-34 [DOI] 10.4049/jimmunol.1501217. [PMID] 26573834.
2016
Editorial.
Bulletin of mathematical biology. 78(12) [DOI] 10.1007/s11538-016-0223-8. [PMID] 27796721.
2016
Editorial.
Bulletin of mathematical biology. 78(1):1-3 [DOI] 10.1007/s11538-015-0134-0. [PMID] 26754090.
2016
Identification of control targets in Boolean molecular network models via computational algebra.
BMC systems biology. 10(1) [PMID] 27662842.
View on: PubMed
2015
A network biology approach to denitrification in Pseudomonas aeruginosa.
PloS one. 10(2) [DOI] 10.1371/journal.pone.0118235. [PMID] 25706405.
2015
Iron acquisition and oxidative stress response in aspergillus fumigatus.
BMC systems biology. 9 [DOI] 10.1186/s12918-015-0163-1. [PMID] 25908096.
2015
Optimal harvesting for a predator-prey agent-based model using difference equations.
Bulletin of mathematical biology. 77(3):434-59 [DOI] 10.1007/s11538-014-0060-6. [PMID] 25559457.
2014
A mathematical model of skeletal muscle disease and immune response in the mdx mouse.
BioMed research international. 2014 [DOI] 10.1155/2014/871810. [PMID] 25013809.
2014
A systems biology approach to iron metabolism.
Advances in experimental medicine and biology. 844:201-25 [DOI] 10.1007/978-1-4939-2095-2_10. [PMID] 25480643.
2014
Steady state analysis of Boolean molecular network models via model reduction and computational algebra.
BMC bioinformatics. 15 [DOI] 10.1186/1471-2105-15-221. [PMID] 24965213.
2014
An algebra-based method for inferring gene regulatory networks.
BMC systems biology. 8 [DOI] 10.1186/1752-0509-8-37. [PMID] 24669835.
2013
Stabilizing gene regulatory networks through feedforward loops.
Chaos (Woodbury, N.Y.). 23(2) [DOI] 10.1063/1.4808248. [PMID] 23822505.
2013
The genome-wide early temporal response of Saccharomyces cerevisiae to oxidative stress induced by cumene hydroperoxide.
PloS one. 8(9) [DOI] 10.1371/journal.pone.0074939. [PMID] 24073228.
2012
Modeling stochasticity and variability in gene regulatory networks.
EURASIP journal on bioinformatics & systems biology. 2012(1) [DOI] 10.1186/1687-4153-2012-5. [PMID] 22673395.
2012
The core control system of intracellular iron homeostasis: a mathematical model.
Journal of theoretical biology. 300:91-9 [DOI] 10.1016/j.jtbi.2012.01.024. [PMID] 22286016.
2011
A mathematical framework for agent based models of complex biological networks.
Bulletin of mathematical biology. 73(7):1583-602 [DOI] 10.1007/s11538-010-9582-8. [PMID] 20878493.
2011
ADAM: analysis of discrete models of biological systems using computer algebra.
BMC bioinformatics. 12 [DOI] 10.1186/1471-2105-12-295. [PMID] 21774817.
2011
Algebraic methods in mathematical biology.
Bulletin of mathematical biology. 73(4):701-5 [DOI] 10.1007/s11538-011-9643-7. [PMID] 21400021.
2011
Bioinformatics tools for cancer metabolomics.
Metabolomics : Official journal of the Metabolomic Society. 7(3):329-343 [PMID] 21949492.
View on: PubMed
2011
Differential gene expression in normal and transformed human mammary epithelial cells in response to oxidative stress.
Free radical biology & medicine. 50(11):1565-74 [DOI] 10.1016/j.freeradbiomed.2011.03.002. [PMID] 21397008.
2011
Regulatory patterns in molecular interaction networks.
Journal of theoretical biology. 288:66-72 [DOI] 10.1016/j.jtbi.2011.08.015. [PMID] 21872607.
2010
Polynomial algebra of discrete models in systems biology.
Bioinformatics (Oxford, England). 26(13):1637-43 [DOI] 10.1093/bioinformatics/btq240. [PMID] 20448137.
2010
The dynamics of conjunctive and disjunctive Boolean network models.
Bulletin of mathematical biology. 72(6):1425-47 [DOI] 10.1007/s11538-010-9501-z. [PMID] 20087672.
2010
Discretization of time series data.
Journal of computational biology : a journal of computational molecular cell biology. 17(6):853-68 [DOI] 10.1089/cmb.2008.0023. [PMID] 20583929.
2009
A general map of iron metabolism and tissue-specific subnetworks.
Molecular bioSystems. 5(5):422-43 [DOI] 10.1039/b816714c. [PMID] 19381358.
2009
A systems biology view of cancer.
Biochimica et biophysica acta. 1796(2):129-39 [DOI] 10.1016/j.bbcan.2009.06.001. [PMID] 19505535.
2009
Algebraic models of biochemical networks.
Methods in enzymology. 467:163-196 [DOI] 10.1016/S0076-6879(09)67007-5. [PMID] 19897093.
2009
Mathematical biology education: beyond calculus.
Science (New York, N.Y.). 325(5940):542-3 [DOI] 10.1126/science.1176016. [PMID] 19644095.
2008
A virtual look at Epstein-Barr virus infection: simulation mechanism.
Journal of theoretical biology. 252(4):633-48 [DOI] 10.1016/j.jtbi.2008.01.032. [PMID] 18371986.
2008
The effect of negative feedback loops on the dynamics of boolean networks.
Biophysical journal. 95(2):518-26 [DOI] 10.1529/biophysj.107.125021. [PMID] 18375509.
2008
Using formal concept analysis for microarray data comparison.
Journal of bioinformatics and computational biology. 6(1):65-75 [PMID] 18324746.
View on: PubMed
2007
A virtual look at Epstein-Barr virus infection: biological interpretations.
PLoS pathogens. 3(10):1388-400 [PMID] 17953479.
View on: PubMed
2007
Comparison of reverse-engineering methods using an in silico network.
Annals of the New York Academy of Sciences. 1115:73-89 [PMID] 17925358.
View on: PubMed
2007
Nested Canalyzing, Unate Cascade, and Polynomial Functions.
Physica D. Nonlinear phenomena. 233(2):167-174 [PMID] 18437250.
View on: PubMed
2007
Reverse engineering of dynamic networks.
Annals of the New York Academy of Sciences. 1115:168-77 [PMID] 17925347.
View on: PubMed
2007
Simulating Epstein-Barr virus infection with C-ImmSim.
Bioinformatics (Oxford, England). 23(11):1371-7 [PMID] 17341499.
View on: PubMed
2004
A computational algebra approach to the reverse engineering of gene regulatory networks.
Journal of theoretical biology. 229(4):523-37 [PMID] 15246788.
View on: PubMed

Grants

May 2020 ACTIVE
Modular design of multiscale models, with an application to the innate immune response to fungal respiratory pathogens
Role: Principal Investigator
Funding: NATL INST OF HLTH NIBIB
May 2020 ACTIVE
Microbial Communities Using Model-Based Multi-Objective Optimization
Role: Principal Investigator
Funding: NATL INST OF HLTH NIGMS
May 2020 ACTIVE
Multiscale modeling of the battle over iron in invasive lung infection
Role: Principal Investigator
Funding: NATL INST OF HLTH NIAID

Education

Ph.D. mathematics
1985 · Northwestern University

Contact Details

Phones:
Business:
(352) 294-2350