In this post I talk about my motivation to complete a degree in statistics during my PhD, and all the failures that went into that decision.
Dr Ingo Dreyer's talk to the modeling community and some takeaways on biological modeling and presenting modeling results.
MBI's online workshop on Mathematical and Computational Methods in Biology will be running all week! See the schedule here (https://mbi.osu.edu/events/mathematical-and-computational-methods-biology). It is also being live streamed here: https://video.mbi.ohio-state.edu/live. Dr. Kim likens simplification of complex biological networks to Picasso's progressive reductionism of a bull. I really enjoyed this talk by Dr Jae Kyoung Kim of KAIST.… Continue reading Jae Kyoung Kim – Analysis of dynamic data: from molecule to behavior – Mathematical and Computational Methods in Biology Workshop
The stereotype of math is of a white-haired professor writing equations on a blackboard. Usually the professor doesn't care about real life or applications. But math is a really broad field. Made by Dominic Walliman. You can purchase here, and download different dimensions here. In the above map, different theoretical or 'pure' concepts have their… Continue reading Map of mathematics: applications to biology and beyond
Six example 3D surface and contour plots using @MATLAB
Last year I held a workshop on collaboration in computational plant biology at pb2019. We went through a few examples of how computational biology is used in a variety of situations, discussed challenges in collaboration and communication. Participants were matched with potential collaborators at the end of the workshop. I received a lot of feedback… Continue reading Workshop on Computational Plant Biology at PB2020 & Opportunities for Participants
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
Most biological systems consist of many moving parts that happen at slightly different timescales. A true-to-life biological model of a sub-network might include dozens of parameters and biological species, regardless of the scale. Constructing a model with an appropriate level of complexity is one of the challenges in mathematical biology. Why is model complexity an… Continue reading Model Complexity in Mathematical Biology
How can we identify a good mathematical model? Over focusing on one aspect of modeling (fitting, inference) may not be the best approach. Here are 6 qualities that defines a good model: Reflects data accurately Not overly complex Has predictive power Consistent with general knowledge of the system Can be studied mathematically Can be simulated… Continue reading What makes a model good?
Programming elements that are used to perform basic mathematical calculations include: Arrays Matrices Equations or Simple functions Math functions See my previous post about the first two here. This post will deal with the latter two. Equations or Simple functions The simplest problem to be solved via programming would be the solution to a single… Continue reading Basic Mathematical Biology Programming Part II