Jason M Knight

Computational Biologist  |  jason@jasonknight.us


I'm a computational biologist/engineer/bioinformatician with several years experience with high-dimensional data analysis. I have a penchant for programming and programming language design with some hardware and entrepreneurial experience mixed in. I also have a wide range of additional interests and hobbies (cryptocurriencies, biohacking, etc.).

I am currently finishing my Ph.D. at Texas A&M University in the Genomic Signal Processing laboratory where I apply tools from modern engineering, mathematics, and statistics in order to understand and treat cancer.

Specifically, I have worked closely with biologists to develop Bayesian statistical models to uncover multivariate interactions among genes from RNA-Seq data. This allows us to classify types of cancer better than nonlinear SVM methods (which themselves are typically considered state of the art). I have also modeled biological regulatory networks using probabilistic Markov models that incorporate pathway knowledge from the biology literature as well as high-throughput data.

In the future, I want to continue using my knowledge and skills to enable principled approaches towards the detection, classification, and treatment of human diseases.

nfkb project image
Genetic Regulatory Network Modeling Using Biological Pathway Knowledge

Every single one of the 40 trillion cells in your body has tens of thousands of genes which are coordinated in a chaotic symphony of regulation for your continued living (homeostasis in biological parlance). For decades now, biologists have been studying this regulatory network piece by piece through a variety of ingenious techniques and a mind-boggling amount of effort.

The trouble is, this biological pathway knowledge is incomplete and sometimes contradictory. Due to this, building predictive models from this knowledge is difficult. In this work, we used a modeling approach embracing the uncertainty through a family of Markov chains (which themselves are a random dynamical system model).

Specifically, we obtained pathways from the biological literature regarding the NF-kB network which is central to cellular inflammatory processes. Then constructing a family of Markov chains from these pathways, we could then make predictions of how the network might evolve under the deletion of several genes under the effect of various stimuli. These predictions were then qualitatively validated against additional biological literature where these experiments were carried out on mouse gene knockout models.

For more information, please consult our paper, and consult my open source Haskell code used to generate the family of Markov models.


nonlinear fit project image
Nonlinear fit of flourescence data


More projects coming soon...



Ph.D. Electrical Engineering
Texas A&M University
Advisor: Edward Dougherty
May 2015 (Expected)

B.S. Biomedical Engineering
Texas A&M University
Minor: Mathematics (summa cum laude)
May 2009

  • Proficient: Julia, Python, Haskell
  • Working-level: C, Javascript
  • In the wings: R, C++, Bash
  • RNA-Seq
    • Alignment
    • Quantification
    • Single Cell
    • Differential Expression
    • Multivariate Analysis
    • Pattern Detection
  • ChIP-Seq
  • RT-qPCR
  • Bayesian inference
    • Classification
    • Bayesian networks
    • Model-based quantization
  • Markov Chain Monte Carlo techniques (including SAMCMC)
  • Monte Carlo Integration

  • 2010 - Innovative Signal Analysis Fellowship
  • 2010 - National Science Foundation Graduate Fellowship - Two Time Honorable Mention
  • 2009 - Texas A&M Electrical and Computer Engineering Departmental Scholarship
  • 2008 - Barry M. Goldwater Scholar - Two Time Honorable Mention
  • 2008 - 1st place in Mays Business School Consulting Competition
  • 2007 - Texas A&M University Scholar
  • 2005 - Texas A&M Presidential Endowed Scholarship