Hej, jag är Daniel, hur mår du?

I am a Systems Biologist focusing on Networks. More than that though, I see myself as a problem solver who seeks out and jumps at opportunity. I am driven to contribute to the understanding of the world for the betterment of society. I am hungry for adventure both in life and my research.

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 regularly to include current & past projects

Follow links for side projects and personal interests.

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

GRN Inference
Systems Dynamics
Machine Learning
Molecular Biology

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

An oversight in how linear ODE models infer GRN has lead to scaling issues which introduce mega-hubs, ie would be mega regulators not witnessed to occur in nature, and very propably an artifact of the inference method.

01Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms Poster, Dataset and Code for use in this ShinyApp

Motivation: Cancer is known to stem from multiple, independent mutations, the effects of which aggregate to drive the cell into a cancerous state. To understand the complex interplay between affected genes, their gene regulatory network (GRN) needs to be uncovered, revealing detailed insights of regulatory mechanisms. We therefore decided to infer a reliable GRN from perturbation responses of 40 genes known or suspected to have a role in human cancers yet whose regulatory interactions are poorly known. Results: siRNA knockdown experiments of each gene were done in a human squamous carcinoma cell line, after which the transcriptomic response was measured. From these data GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. The best GRN was shown to be significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes but subjected to double perturbations. It agrees with many known links in addition to predicting a large number of novel interactions, a subset of which were experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.

03NestBoot: A Generalized Framework for Controlling FDR in Gene Regulatory Network Inference Publication and Code

Motivation: Inference of Gene Regulatory Networks (GRNs) from perturbation data can give detailed mechanistic insights of a biological system. Many GRN inference methods exist, but the topology of their estimates tend to be sensitive to changes in method specific parameters. Even though the inferred network is optimal given the parameters, it has been shown that many links are wrong or missing if the data is not informative. To make GRN inference reliable, a method is needed to estimate the support of each predicted link as the method parameters are varied. Results: To achieve this we have developed a method called nested bootstrapping, which applies a bootstrapping protocol to GRN inference, and by repeated bootstrap runs assesses the stability of the estimated support values. To translate bootstrap support values to false discovery rates we run the same pipeline with shuffled data as input. This provides a general method to control the false discovery rate of GRN inference that can be applied to any setting of inference parameters, noise level, or data property. We evaluated nested bootstrapping on a simulated dataset spanning a range of such properties, using the LASSO, Least Squares, and RNI inference methods. An improved inference accuracy was observed in almost all situations. The method is part of the GeneSPIDER package, which was also used for generating the simulated networks and data, as well as running and analyzing the inferences.

04GeneSPIDER: gene regulatory network inference benchmarking with controlled network and data properties Publication and Code

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.
Some of my favorite shots from recent travels
Most of my works from over the years; n.b. no formal training whatsoever

07 Read

If We Can Keep It: A Brief, 300-Year History of the Fall of the Republic
Behave: The Biology of Humans at Our Best and Worst
There There
The Tangled Tree: A Radical New History of Life
Dreams of a Final Theory: The Scientist's Search for the Ultimate Laws of Nature
Empire of the Summer Moon: Quanah Parker and the Rise and Fall of the Comanches, the Most Powerful Indian Tribe in American History
AI Superpowers: China, Silicon Valley, and the New World Order
Capital in the Twenty-First Century
On China


Daniel's favorite books »


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