Guide Introduction to Modeling for Biosciences

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Lecture 1. Introduction

Following my D. I left OGT after receiving a commission from Cambridge University Press to write Microarray Bioinformatics , and worked as a free-lance bioinformatics consultant during that time.

BioSciences

I teach on a range of modules associated with mathematical and computer modelling in the biological and environmental sciences. We use mathematical, computing and statistical techniques to build predictive models for biological systems. There are three main areas of activity:. We try, where possible, to work on projects with active collaborations with experimental biologists.

While our main focus is on microbiology, we are happy to foster collaborations with scientists working in any area of biology, and this range is reflected in our publications. Find us Campus map Room Locations on Campus [pdf file]. Connect with the University of Nottingham through social media and our blogs. Campus maps More contact information Jobs. School of Biosciences Study with us Welcome Excellence in research Services for business Working with schools Facilities and equipment Equality, diversity and inclusion Latest news Events calendar People.

Molecular Bioscience/Cell and Molecular Biology

Teaching Summary I teach on a range of modules associated with mathematical and computer modelling in the biological and environmental sciences. There are three main areas of activity: Antimicrobial resistance. We use mathematical and computer models at both molecular and population levels to study mechanisms for, and spread of, antimicrobial resistance. Current work is focussed on the emergence of antimicrobial resistance in the environment, especially in agriculture.

Table of Contents

Past work has included modelling plasmid regulation, and models for molecular mechanisms for antimicrobial metals, including zinc and mercury. We have also carried out in silico evolution of gene network responses to antimicrobial agents. Model-driven data analysis. We combine mathematical models with both frequentist and Bayesian methods with the aims of best interpretation of experimental data.

Applications have included bioluminescent reporter data, Biolog phenotype arrays and metagenomics sequencing data, with applications in brewing, bioenergy, food safety and antimicrobial resistance. Quantitative bioinformatics. We retain an interest in using statistical and machine learning techniques as applied to large scale quantitative data sets from Omics technologies, including transcriptomics, proteomics and metabolomics. Regulatory feedback response mechanisms to phosphate starvation in rice npj Systems Biology and Applications.

Guide to Simulation and Modeling for Biosciences

Removal of copper from cattle footbath wastewater with layered double hydroxide adsorbents as a route to antimicrobial resistance mitigation on dairy farms. The Science of the total environment. DOCX Figshare. Advances in microbial physiology. Multidrug resistant, extended spectrum beta-lactamase ESBL -producing Escherichia coli isolated from a dairy farm. FEMS microbiology ecology. This last example also demonstrates how ABMs can be used to simulate evolution in a biologically realistic way.

This chapter provides the reader with a practical introduction to agent-based modeling via the Repast Simphony Agent Based Modeling toolkit. Using examples of agent-based models from an earlier chapter, we look in detail at how to build models using the Groovy programming language, which is based on Java. We illustrate some of the ways in which a toolkit such as Repast considerably simplifies the life of the modeler by providing extensive support for agent creation, model visualization, charting of results, and multiple runs. This chapter introduces the reader to the basic ideas underlying ordinary differential equations.

Knowledge of the basic rules of differentiation and integration is assumed; however, one of the main objectives of the chapter is to convey to the biologist reader, in an intuitive way, the basic idea of infinitesimal change and differentiation. The second main aim of this chapter is to provide an introduction to ordinary differential equations. The emphasis of this chapter is on usability. By the end of the chapter the reader will be able to formulate basic, but practically useful, differential equations; have a grasp of some basic concepts, including stability and steady states ; and will also have an understanding of some basic methods to solve them.

Furthermore, the reader will be able to critically evaluate differential equation models she may encounter in the research literature. This chapter describes the use of the free, open-source computer algebra system Maxima.

BioSciences < Rice University

Maxima is a system similar to Maple and Mathematica. The use of Maxima is illustrated by a number of examples. Special emphasis is placed on practical advice on how the software can be used, and the reader is made aware of difficulties and pitfalls of Maxima. The chapter also demonstrates how to use Maxima in practice by walking the reader through a number of examples taken from earlier chapters of the book.

Biological Sciences

One difficulty that we face when confronted with this task, however, is that the mathematical background of undergraduate students is very often deficient in essential concepts required for dynamic mathematical modelling. In this practical module, students are introduced to the basic techniques of mathematical modelling and computer simulation from a Systems Biology perspective. The systemic approach to the study of living things is not new in the realm of biology, as it dates from the early twentieth century Von Bertalanffy and Woodger Von Bertalanffy, L.

However, it has been largely absent from most degree programmes in biology, which have been dominated by the reductionist approach. There is no room here to analyse the reasons behind this state of affairs, which can also be observed in other branches of science, but it should be noted that, in the case of biology, the need for a paradigm shift from the reductionist to the systemic approach is particularly pressing. In this context, Systems Biology SB must be understood as an approach to the study of living things that is different from the reductionist approach, but firmly based on the achievements and results of the latter.

SB is an interdisciplinary approach to the study of living things, which seeks to explain and understand their functions and behaviours over time by drawing on both the knowledge of their elements and the usually non-linear relations that exist between them. Model building, is a central part of any scientific endeavour, and one way to incorporate the systemic approach into biology degree programmes is by teaching this skill. In fact, it can be argued that the development of the ability to build models lies at the core of many sciences education programmes.

What SB adds to the issue of modelling is the stress it places on mathematical formulation. This trend is already seen not only in the most mathematical branches of biology e. Building a model is a creative and rigorous task that involves the integration of knowledge and assumptions and requires intuition, imagination and independence of thought. Modelling, either in its classical version the conceptual modelling or in its more formalised form the mathematical one involves asking the questions, choosing the appropriate conceptual framework for formulating and testing hypotheses and making accurate assumptions and simplifications.

Accordingly, teachers of biology need tools that are suitable for transferring the principles and techniques of model building to their students. However, faced with this need, we encounter the not insubstantial problem that the mathematical background of undergraduate students is very often deficient in essential concepts required for mathematical modelling dynamics.

The challenge posed by this situation is: how do we introduce students of biology to the use of mathematical modelling? Here, I describe a practical exercise designed to introduce mathematical modelling and computer simulation to students of biology. The aim is to introduce mathematical modelling, in the context of an SB perspective, as a tool for integrating information and exploring the dynamics of the modelled system. The course described here has been informed by iterative changes to the protocol used over more than five consecutive years — , to teach a medium-sized group of around 20 students within a degree programme where students are normally taught a range of pure and applied biology subjects.

The overall objective of the training proposal is to help undergraduate students of biology acquire an understanding and appreciation for the value and limitations of mathematical models and numerical integration methods. In particular, we seek to make students proficient in: i understanding and integrating the fundamental issues of modelling in biology; ii practicing converting diagrams into computable mathematical models; iii computing models in scenarios representing different environmental conditions or states; iv presenting, discussing and interpreting the results.

The module is widely applicable and could be taught in almost any course of any degree in science or engineering, provided that students have completed the first year of a science degree and at least one semester of biochemistry. This module has been taught by the author in two different programme locations. Originally, it was designed for and given to undergraduate biology students of the second year.

At this point students were conversant in the fundamentals of mathematics, physics, chemistry and biology, as well as biochemistry, microbiology and cellular biology.

Based in this experience it was also taught for engineering and science students, from mathematics to physics both graduated and undergraduate with similar results. The first session 60 minutes is devoted to the introduction of the concept of the model and its role in the scientific process. It explains what a model is, highlighting the fact those verbal arguments, graphs and pictures are different types of models, although of limited capacity in comparison to mathematical models and the computer simulations derived from them.

Subsequently, students are prompted to think about what models can be used for, what the advantages and limitations of any model might be and what is needed for a model to be useful. Models are not an aim in themselves, but a tool towards achieving the understanding of a biological function or its underlying mechanisms. At this point, students are ready to understand the value of an integrating modelling into scientific studies and thus to be introduced to the main tenets of SB.

This seminar does not need to be tied directly to the subsequent practical exercise; in fact, it can be taught some time before. As assigned reading for this session, students are given the introductory chapter of an SB textbook Klipp et al. Concepts, Implementation and Application. Future delivery of the session will also employ the text recently published by Voit Voit, E. The second session 60 minutes is intended to present the basic concepts and tools used in the development and simulation of biological models.

First, the intuitive notion of instantaneous velocity and how this translates mathematically into the concept of the derivative is presented. On this basis, it is possible to start the representation of kinetic processes by differential equations. Figure 1 shows the metabolic pathway that is used for this purpose. It consists of a metabolic pathway involving three coupled reactions, two variables and a positive feedback loop.

This scheme is simple enough to be accessible to most of the students and at the same time rich enough in terms of features and dynamic behaviour to be of pedagogical value. In fact there are some well known metabolic systems that are well represented for this scheme of reactions.

These are the cases of the observed activation of the glycolitic enzyme phosphofructokinase by one of its products that prompts a variety of different dynamic behaviour in the glycolitic pathway and the feedback activation of the intracellular cAMP synthesis by the extracellular cAMP that operates in the Dictiostelium discoideum intercellular communication. In both cases the real systems displays the dynamics behaviour showed by the proposed regulatory structure. Figure 1 Scheme of the metabolic pathway used for the model presentation and selection of processes, rate expressions, variables, parameters and regulatory features and its translation into a set of ordinary differential equations.

A and B variables represent two distinct species metabolites, species, etc. They interact in such a way that any increase in B activates its own synthesis, according with the V1 rate equation.