The London Mathematical Society Computer Science Colloquium 2014
Computational and Mathematical Modelling for Improved Understanding of Biological Systems
Wednesday 29 October 2014
In recent years the development and application of computational and mathematical models of a range of processes has supported significant advances in our understanding of a wide variety of complex biological systems. The colloquium provides examples of this approach, from biological networks and biochemical signalling pathways through to models of a simple animal and of the human brain. In each case the use of techniques from mathematics and computer science allows basic questions about complex biological processes and systems to be addressed.
11:00 – 12:00 Netta Cohen (University of Leeds)
Adaptive search behaviours in worms
The nematode worm C. elegans is a relatively simple animal with only 302 nerve cells and a set of well characterized stereotypical behaviours. To survive, this animal must implement stochastic navigational strategies that allow it to search for favourable conditions in complex, unknown environments. One key challenge is to understand what dynamics the animal's nervous system supports, and how the neural computations give rise to such non-trivial behaviours. In this talk, Professor Cohen will begin with an introduction to the animal, its nervous system and its behaviours. She will then present computational models of the neural control of navigation related tasks faced in different kinds of environments (e.g., smooth gradients, discrete spatial regions, or rugged landscapes) and extend the models to include observed forms of sensory adaptation and learning.
Finally, she will discuss some of the conflicts and payoffs that arise if the same circuit is called on to effectively navigate these very different environments.
12:00 – 13:00 Jane Hillston (University of Edinburgh)
Embedding machine learning in formal stochastic models of biological processes
Formal modelling languages such as process algebras are effective tools in computational biological modelling. However, handling data and uncertainty in these representations in a statistically meaningful way is an open problem, severely hampering the usefulness of these elegant tools in many real biological applications.
Professor Hillston will present ProPPA, a process algebra which incorporates uncertainty in the model description, supporting the use of Machine Learning techniques to integrate observational data in the modelling. She will explain how this is given a semantics in terms of a generalisation of Constraint Markov Chains, and demonstrate how this can be used to perform inference over biological models.
13:00 – 14:00 - LUNCH
14:00 – 15:00 Aldo Faisal (Imperial College London)
From noise in the nervous system to the variability of behaviour
Our research questions are centred on a basic characteristic of biological systems: noise, uncertainty or variability in behaviour. Variability can be observed across many levels of biological behaviour: from the movements of our limbs, the responses of neurons in our brain, to the interaction of biomolecules. Such variability is emerging as a key ingredient in understanding biological principles (Faisal, Selen & Wolpert, 2008, Nature Rev Neurosci) and yet lacks adequate quantitative and computational methods for description and analysis. Crucially, we find that biological and behavioural variability contains important information that our brain and our technology can make us of (instead of just averaging it away): The brain knows about variability and uncertainty and it is linked to its own computations. Therefore, we use and develop statistical machine learning techniques, to predict behaviour and analyse data.
15:00 – 16:00 Luca Cardelli (Microsoft Research & University of Oxford)
Morphisms of reaction networks
The mechanisms underlying complex biological systems are routinely represented as networks. Network kinetics is widely studied, and so is the connection between network structure and behavior. But it is the relationships between network structures that can reveal similarity of mechanism.
We define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties can be checked statically on the structure of the networks. In particular we can determine statically that a complex network will emulate a simpler network: it will reproduce its kinetics for all corresponding choices of reaction rates and initial conditions. We use this property to relate the kinetics of many common biological networks of different sizes, also relating them to a fundamental population algorithm.
Thus, structural similarity between reaction networks can be revealed by network morphisms, elucidating mechanistic and functional aspects of complex networks in terms of simpler networks.
Limited funds are available to help with students’ travel costs. Further details are available from Duncan Turton at the Society.