mathematical biology, statistics, and more
Shelby Scott and Miranda Chen recently published the article Technology as a tool in teaching quantitative biology at the secondary and undergraduate levels. You can find the paper here. In this interview, the authors describe what they see as the next steps for integrating quantitative methods into biological curriculum and tons of online resources to get you started. This information will help… Continue reading Interview: Technology as a tool in teaching quantitative biology
A couple of days ago Magdalena Julkowska (https://twitter.com/mmjulkowska) asked in a tweet what my opinion is about a recent article
Would love to hear what @ronaldpierik@GelderenKasper think about this paper!
— Magdalena Julkowska (@mmjulkowska) May 17, 2018
So Magda, this one is especially for you 🙂
She asked this, because she knows that this topic is of great interest to me. So what is it?
The article “Root‐expressed phytochromes B1 and B2, but not PhyA and Cry2, regulate shoot growth in nature”, by Oh et al., from the Ian Baldwin lab, was recently published in Plant Cell and Environment. It tries to address the question what the role is of the phytochrome light receptors in plant roots, which obviously grow in an environment of relative darkness.
This is a topic which we ourselves have discussed as well, although as a sideline from the main part, in two recent articles:…
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a chemist Or at least that’s what I’d probably say if you caught me on the street. Fact is, for modern scientists, there can be a painstakingly high degree of specialization, minutiae, and technical training entangled with their work. Some areas of science are somewhat relatable due to the high amount of news coverage –… Continue reading Guest post: Who am I?
Ask me anything anonymously (or non-anonymously) using this Google form: https://goo.gl/forms/JIAIGvvLYATAuEjv2 You can also ask questions on the blog or on the Plantae BDCI network!
The two most popular Markov chain Monte Carlo sampling algorithms are Gibbs sampling and Metropolis Hastings. These algorithms produce Markov chains. Numbers inside a Markov chain are dependent on only the previous number. In the context of sampling, we check the probability of the proposed value based on only the probability of the current value,… Continue reading Introduction to Bayesian Statistics, Part 3
Mathematical modeling is a relatively new field. You may be more aware of a subfield of biology called bioinformatics or computational biology. These tend to deal with larger data sets, studying them using more algorithmic methods. Computational biology also refers to mathematical biology, or an even mixture of mathematics and computer science as applied to… Continue reading Why do we need mathematical modeling in biology?
When I was a freshman I took calculus, like many biology majors. I was told I would fail, so naturally I absolutely had to get an A. My class was relatively small, about 50 students. A good variety of students, including gender and race, since it was in Louisiana. There was a group of loud… Continue reading How I was affected by stereotypes of women in math and philosophy
As mentioned in Part 1, in Bayesian statistics you summarize a priori knowledge in the prior, and your data in the likelihood. The prior distribution is often chosen based on analytical convenience, while the likelihood is chosen based on the underlying sampling distribution (read about some appropriate distributions here). Multiplying these together produces the posterior distribution. Probability… Continue reading Introduction to Bayesian statistics, Part 2
Anxiety is tricky to control because we often feel like obsessing over what can go wrong can help us respond in the many possible events. To reduce anxiety, you need to stop this thought process. This can be difficult in graduate school because of the large number and variety of tasks required for success. After… Continue reading 6 steps to reducing anxiety through task management in graduate school
i am become computational 