Daniel Morgan

Hi, I'm Daniel.

Problem Solver. Explorer. Scientist.

I am working towards my doctorate in The Department of Biochemistry & Biophysics at Stockholm University. I studied in the Nordling Lab in the Mechanical Engineering department of the National Cheng Kung University during the spring of my third year as a visiting scientist. I received my Masters of Bioinformatics from The Ohio State University, and my Bachelors in Molecular Biology from Miami University.

This page is updated quarterly to include current & past projects

Follow links for side projects.

Work

Visiting Scholar
Feb 2017 - May 2017
Designing novel systems approach to controlling and balancing input and output noise in linear models used for network reconstruction
Bioinformatics Data Analyst
May 2013 - September 2014
Remote testing and data analysis and extraction using novel web application with R back-end

Skills

Molecular Biology
Systems Dynamics
Machine Learning
Probabilistic Graphical Modeling

Education

Stockholm University
Doctor of Philosophy
January 2015 - December 2019
I am working on several projects surrounding the reverse engineering, or inference, of gene regulatory networks, with an interest in downstream drug repositioning in the Sonnhammer Lab at SciLifeLab in conjunction with Torbjörn Nordling at National Cheng Kung University
The Ohio State University
Master of Science
August 2012 - December 2014
Studied and worked with interest in drug repositioning, with projects investigating primary bladder and lung cancer samples. Thesis: Gene Co-Expression Network Mining Approach for Differential Expression Analysis.
Miami University
Bachelor of Science
August 2006 - January 2011
Worked in the Fisk Lab, as well as two summers in the Letterio Lab at Case Western Reserve University

Techniques

Microarray
qPCR
RNA-Seq

Languages

MATLAB
R
Python
Copasi
Mathematica

Projects

I have worked in collaborating with other students, namely Andreas Tjärnberg, on the GeneSpider Package for MATLAB, which hopes to tackle a few key issues in modern network inference. Inference of gene regulatory networks (GRNs) is a central goal in systems biology. It is therefore important to evaluate the accuracy of GRN inference methods in the light of network and data properties. Although several packages are available for modelling, simulate, and analyse GRN inference, they offer limited control of network topology together with system dynamics, experimental design, data properties, and noise characteristics. Independent control of these properties in simulations is key to drawing conclusions about which inference method to use in a given condition and what performance to expect from it, as well as to obtain properties representative of real biological systems.

02 Paint

A few years ago I was a foolhardy artist.

03 Read

Homo Deus: A Brief History of Tomorrow
Sapiens: A Brief History of Humankind
Age of Ambition: Chasing Fortune, Truth, and Faith in the New China
Who Rules the World?
Blood Oil: Tyrants, Violence, and the Rules that Run the World
The Making of the Fittest: DNA and the Ultimate Forensic Record of Evolution
One Hundred Years of Solitude
The Magic Mountain


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CONTACT

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D.C. Morgan Portfolio