Student Directory

Steven Steinway

Class Year: M3

 

Department/Institute/Center(s):

  • M.D./Ph.D. Program

 

Contact Information:

 

Biographical Information:

Hometown: Florham Park, NJ

 

Education:

Undergraduate Education:  BS/09 Biology/College of New Jersey

 

Research Interests:

Research Interest:  Network modeling, dynamic analysis, biological big data, network science, biological computing, epithelial-to-mesenchymal transition, tumor invasion & metastasis, gut microbiome 


Advisor: Thomas Loughran co-advisor (HY); Reka Albert co-advisor (UP)
Graduate Program: Molecular Medicine

Thesis: "PREDICTIVE NETWORK MODELING AND EXPERIMENTATION IN COMPLEX BIOLOGICAL SYSTEMS: APPLICATIONS TO CANCER AND INFECTIOUS DISEASE"

Description: 

Biology is incredibly complex – at the molecular, cellular, tissue, and population level, there exists a tremendous number of discrete interacting components tightly regulating the processes that sustain life. Biological systems have traditionally been viewed in a reductionist manner often literally (and metaphorically) through a magnifying glass, leading to insight into how the individual parts work. Network theory, on the other hand, can be used to put the pieces together, to understand how complex and emergent behaviors arise from the totality of interactions in complex systems, such as those seen in biology. Network theory is the study of systems of discrete interacting components and provides a framework for understanding complex systems. A network-focused investigation of a complex biological system allows for the understanding of the system's emergent properties, for example its function and dynamics. Network dynamics are of particular interest biologically because biological systems are not static but are constantly changing in response to perturbations and environmental stimuli in space and time. Systems level biological analysis has been aided by the recent explosion of high throughput data. This has led to an abundance of quantitative and qualitative information related to the activation of biological systems, but frequently there is still a paucity of kinetic and temporal information. Discrete dynamic modeling provides a means to create predictive models of biological systems by integrating fragmentary and qualitative interaction information. Using discrete dynamic modeling, a structural (static) network of biological regulatory relationships can be translated into a mathematical model without the use of kinetic parameters. This model can describe the dynamics of a biological system (i.e. how it changes over time), both in normal and in perturbation (e.g. disease) scenarios. In my thesis work I apply network theory and discrete dynamic modeling integrated with experimental laboratory analysis to understand biological diseases in three contexts:

1. Epidermal derived growth factor receptor (EGFR) signaling in cancer. We translate this model into two types of discrete models: a Boolean model and a three-state model. We show how the effects of an EGFR inhibitor (such as the drug gefitinib) can suppress tumor growth, and we model how genomic variants can augment the effect of EGFR inhibition in tumor growth, so called "personalized cancer treatment".

2. Epithelial-to-mesenchymal transition (EMT), a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. We identify cross-talk mechanisms and identify combinatorial interventions that we test experimentally using siRNA screening in liver cancer (hepatocellular carcinoma).

3. A model of the enormous ecological community of bacteria that live in our intestines, collectively called the "gut microbiome". This model is used to understand the effect of antibiotic treatment and opportunistic C. difficile infection (a devastating and highly prevalent disease entity) on the native microbiome and predict therapeutic probiotic interventions to suppress C. difficile infection. We integrate this modeling with another type of modeling, genome scale metabolic network reconstructions, to understand metabolic differences between community members and to identify the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of my computational model, 

Meeting Presentations

  • 2007
    REU Bioinformatics, Research Symposium, Loyola U, Chicago, IL
    Semiannual Student Research Symposium, C of NJ
  • 2008
    11th Annual Celebration of Student Achievement, C of NJ
    Bioinformatics Summer Inst. Research Symposium, U of MN
  • 2012
    Penn State College of Medicine Graduate Research Forum, Hershey, PA (poster)
    AACR Chemical Systems Biology meeting, Boston, MA (oral)
  • 2014
    Systems Biology of Human Disease Meeting, Boston, MA (poster)
  • 2015 International Society for Computational Biology Conference on Regulatory and Systems Genomics (ISCB RECOMB REG SYS BIO) meeting (oral)

 

 

Publications:

Steinway SN, Zañudo JGT, Michel P, Feith DJ, Loughran TP Jr, Albert R. Combinatorial Interventions Inhibit TGFß-Driven Epithelial-to-Mesenchymal Transition and Support the Existence of Hybrid Epithelial-Mesenchymal Phenotypes. Submitted

Steinway SN, Wang RS, Albert R. Discrete dynamic modeling: A network approach for systems pharmacology. Systems Pharmacology and Pharmacodynamics. (Book Chapter; In Press). 2015.

Steinway SN, Biggs MB, Loughran TP Jr, Papin JA, Albert R. Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome. PLoS Comput Biol. 2015 Jun 23;11(5):e1004338. doi: 10.137/journal.pcbi.1004338. eCollection 2015 May. PMID: 26102287 PMCID: PMC4478025

Steinway SN, Dang H, You H, Rountree CB, Ding W. The EGFR/ErbB3 Pathway Acts as a Compensatory Survival Mechanism upon c-Met Inhibition in Human c-Met+ Hepatocellular Carcinoma. PLoS One. 2015 May 22:10(5):e0128159. doi: 10.1371/journal.pone.0128159. eCollection 2015. PMID: 26000702 PMCID: 4441360

Dang H, Steinway SN, Ding H, Rountree CB. Induction of tumor initiation is dependent on CD44s in c-Met+ hepatocellular carcinoma. BMC Cancer. 2015. PMID: 25886575. PMCID: PMC4380258

Steinway SN, Zañudo JG, Ding W, Rountree CB, Feith DJ, Loughran TP Jr, Albert R. Network modeling of TGFß signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition reveals joint sonic hedgehog and Wnt pathway activation. Cancer Res. 2014. PMID: 25189528.

Ding W, Dang H, You H, Steinway S, Takahashi Y, Wang HG, Liao J, Stiles B, Albert R, Rountree CB (2012) miR-200b restoration and DNA methyltransferase inhibitor block lung metastasis of mesenchymal-phenotype hepatocellular carcinoma Oncogenesis 1:e15.doi:10.1038/oncsis.2012.15 PMCID:  PMC3412647

Steinway SN, LeBlanc F, Loughran TPJr (2014) The pathogenesis and treatment of large granular lymphocyte leukemia Blood Rev 28(3):87-94  PMCID:  (no number)

Steinway SN, Loughran TP (2013) Targeting IL-15 in large granular lymphocyte leukemia Expert Rev Clin Immunol 9(5):405-8  PMID: 23634735  PMCID:  PMC3889194

Steinway SN, Dannenfelser R, Laucius CD, Hayes JE, Nayak S (2009) JCoDA:  A tool for Detecting Evolutionary Selection, BMC Bioinformatics 11:284  PMCID:  PMC2887424

Springer NM, Eichten S, Smith A, Steinway SN, Kaeppler SM (2009) Genome wide distribution and intraspecific variation of a middle-copy maize retrotransposon, SPRITE  Maydica  2009 54:417 PMCID:  (no number)

Steinway SN (2009) In Silico:  Retrieval and Cataloging of Genbank DNA Sequences Adjacent to Interspersed Repetitive Elements (2009) TCNJ J of Student Scholarship

 

 

 

Steven Steinway

 

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