mathematical modelling Archives - Digitizing Biology

July 26, 2021July 23, 2021

Mathematics and Biology (the Pharma Vision)

I began this blog almost exactly three years ago. My goal was two-fold: first, to demonstrate thought leadership in an area in which I was then founding a company; and, second, to have a demonstrated track-record rather than a picture-perfect pitch-deck. This latter is clearly a sub-goal of the first.

In those early days, I began a series of articles on Mathematics and Biology. I began with a comparison of the two fields. From there I moved on to discussing the people and skills involved, essentially why mathematicians so rarely do biology. I then discussed the outlier which is bioinformatics. I had two further articles planned in the series, after which point I wanted to attempt a synthesis. The missing articles are on the Microbiome and Quantitative Systems Pharmacology (QSP). These will remain missing articles for now, time has moved on and my thoughts on all of these topics have progressed enormously.

A few weeks ago I published a futurecast about using virtual patient’s for pharma drug development. There was a key idea buried in that article which in essence is my current synthesis perspective on mathematics and biology. I want to highlight this idea here and flesh it out slightly.

August 24, 2020August 22, 2020

Modelling the Modeller

Something I’ve struggled with on and off over the 20 years that I have been making mathematical models is explaining those models to others. I have tried to bring people along and develop their understanding. But mainly what I observed was that, some people just got it and others did not.

I have certainly improved my own skill at explaining. This comes down to having streamlined stories and simpler take-home messages. Telling a clearer story certainly improves my audiences’ self-satisfaction, but ultimately some of them get the whole message and others do not.

June 22, 2020June 22, 2020

Narrative and Decision Models

Since I managed to break my writers’ block on decision making models last week I want to follow-up with a brief discussion on the use of Narrative in presenting decision models to an audience.

In my first article on decision making models I emphasized that a model must serve a purpose. In explaining our models to others I want to highlight that there are two purposes behind explaining a model; the first is to convince the audience; the second is to convey insights into the model. This is the opposite ordering of how scientifically-trained modellers typically think about communicating results, but it is by far-and-away the prioritisation of most top scientific communicators around the world.

June 16, 2020September 28, 2020

Decision Making Using Models 3.0

This is my third attempt, over the course of 9 months, to write this article. The first attempt foundered on my desire to go into detail on whether explanation or explanability is a good characteristic of a model or not. I confess, this was overly motivated by my personal frustration at having worked with somebody who, “never let the facts get in the way of a good story.” The second attempt got lost in a forest of anecdotes from previous projects. I was trying so hard to knit them together that I failed to make a point. Today, I want to focus on the single most important thing that I have learned about developing decision making models.

March 10, 2020March 10, 2020

ML Embeddings and the Neuronal Code

I had the opportunity to talk recently with a relatively advanced researcher in machine learning methods. The conversation turned briefly to the study of embeddings when he mentioned that most of his work involves things that can be embedded in Euclidean space. Since I’ve been spending a bit of time thinking about embeddings recently, I asked him some questions to get the official ML take on the subject. I was resonably gratified to learn that – although most ML engineers don’t think much about embeddings – the research on this topic considers the embedding to be tightly bound to the network architecture. It is not possible to study abstract embeddings, divorced from applications. I fully agree with this point-of-view.

January 6, 2020January 3, 2020

Invited Speaker: Dynamics of Immune Responses

I have been invited to speak at the Dynamics of Immune Responses workshop/seminar/conference in May-June 2020. The invitation arose through my previous efforts to found a company in this space.

There is a growing awareness in the field of immunology of the potential for using mathematical techniques. The wedge-issue here is the cascade of data appearing via new cytometry techniques; large-data looks like a math issue to most people. I of course come from the other side of a spectrum – everything looks like a math issue to me – I wanted to stimulate drug development which engages with immune system dynamics by founding my company.

October 7, 2019October 5, 2019

New Project: Causal Inference

I am beginning a new project this week, the topic is Causal Inference. This is something I have been reading about, and wrestling with, for quite some time. Now seems a good point to take some time out, form a project, and see what I can get done on the topic.

August 5, 2019August 3, 2019

Why I write

I sometimes see myself as a slow learner. I am extremely fast at deep-thinking, which somewhat disguises this fact, but I learn things from the ground up. Until I can think a topic through I sometimes feel unsure about operating from an incomplete understanding.

When I worked in academia I prefered to learn rather than to force my opinions on others. Everybody seemed reasonably smart, and they were absolutely convinced of their own correctness, and so I listened and learned. Continue reading “Why I write”

June 10, 2019July 11, 2019

Mathematics and Biology III – Bioinformatics

When I sat down in Summer 2018 to begin my blog one of my goals was to write approximately 5 definitive articles about Mathematics and Biology. So far, I have been pretty hard on the efforts in both fields to come together. I began with a review of the very different world-views inherent in the two subjects – combined with a call to arms for likeminded people to come and help out. I followed this with a more practical consideration of the repertoire of techniques necessary and the career constraints, which actively work against combining these two disciplines. Today I want to consider the shining example of bioinformatics – the one area in which mathematics is clearly being used in biology and which demonstrates a clear career path.