Tag: computational biology

Math Modeling

Jae Kyoung Kim – Analysis of dynamic data: from molecule to behavior – Mathematical and Computational Methods in Biology Workshop

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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

Biology, Computation, Data analysis, Math Modeling

Workshop on Computational Plant Biology at PB2020 & Opportunities for Participants

iambecomecomputational

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

Biology, Computation, Data analysis, Math Modeling, Teaching

Introduction to modeling: parameter estimation in R

iambecomecomputational

This code introduces how to perform parameter estimation for a system of differential equations in R. First, the necessary packages and data are imported using code previously introduced in a previous post. R code available here.  Due to computational constraints, the mathematical model we are using to fit to the data includes product inhibition. If product inhibition doesn't… Continue reading Introduction to modeling: parameter estimation in R

Biology, Computation, Math Modeling, Teaching

Modeling demo: photosynthesis

iambecomecomputational

This demo is intended to help students understand the connection between photosynthetic pigment function, light energy, and plant growth. The relative absorption of 8 wavelength ranges can be modified to represent an absorption spectra. These absorption spectra could be simplified to include one or two specific colors, or approximate an absorption spectra obtained in lab.… Continue reading Modeling demo: photosynthesis

Data analysis, Statistics

Computational Plant Science – Clustering in R Tutorial

iambecomecomputational

Last week was the Plantae Seminar "Computational Plant Science - Science at the Interface of Math, Computer Science, and Plant Biology with Alexander Bucksch": Dr. Bucksch mentions using two clustering techniques - B splines and K-means clustering. I've discussed K-means clustering in a previous post to analyze predictors of success in Settlers of Catan. B splines… Continue reading Computational Plant Science – Clustering in R Tutorial

Biology, Computation, Data analysis, Math Modeling, Misc, Statistics, Visualization

Ask me anything!

iambecomecomputational

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!

Biology, Computation, Math Modeling

Why do we need mathematical modeling in biology?

iambecomecomputational

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?

Data analysis, Statistics

Introduction to Bayesian statistics, Part 2

iambecomecomputational

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