Title: Computational inference of biopathway dynamics and structures
Funder: Engineering and Physical Sciences Research Council

Abstract: The mathematical modelling of regulatory interactions and signalling processes in living cells is a growing research area, aiming to elucidate the molecular mechanisms that give rise to complex biological phenomena. Examples include circadian clock models describing how plants predict the length of night and adjust their metabolism to prevent carbon starvation before dawn, or carcinogenesis models aiming to explain how aberrant cellular signalling pathways lead to tumour growth and metastasis. Ambitious current approaches in systems biology aim to develop mechanistic models of the relevant cellular networks, using methods from chemical kinetics and control theory. However, due to the large number of chemical kinetic parameters, inference remains an extremely challenging problem, restricting current applications to small a priori identified model pathways. The objective of the proposed research project is to advance the current state of the art of computational/statistical inference in mechanistic models, with a particular focus on applications in systems biology. To this end, we aim to explore and hone a portfolio of methods combining ideas based on (i) gradient matching, (ii) modularization, (iii) parallel tempering, and (iv) focus statistics. The idea of gradient matching (i) is to avoid a computationally expensive explicit solution of the differential equations and instead infer kinetic parameters that give the best agreement between the gradients predicted from the differential equations and those obtained from the tangent to the interpolant of the data. The aim of modularization (ii) is to decompose a complex system into a collection of simpler weakly coupled subsystems for which inference is less challenging, and then reduce inconsistencies between the subsystems in an iterative manner. Parallel tempering (iii) proceeds by carrying out inference on a set of several increasingly smoothed (tempered) versions of the mismatch function in parallel, as a way to avoid suboptimal local optima. As an alternative to smoothing, we aim to adopt ideas from approximate Bayesian computation (iv) and replace the data in parallel chains by sets of focus statistics to extract relevant patterns from the data. This mimics heuristic procedures that are currently carried out by biological modellers, who aim to find chemical kinetic parameters that match certain signatures in the data, like phase shifts, frequency variations, amplitude alterations, etc. All approaches, in isolation and in combination, aim to reduce the computational complexity at a high level of accuracy, thereby enabling an application of inference in mechanistic models to larger and more complex systems For regulatory networks whose structure is a priori unknown, we precede the above procedures with novel structure learning algorithms from machine learning, aiming for fast search through network topology space based on abstract models of molecular interactions. For both structure learning with machine learning methods and kinetic parameter inference for mechanistic models we will exploit modern PC clusters for parallel processing. The results of our research will be implemented in a user-friendly software toolbox that will be integrated into GLAMA, a widely used systems biology and polyomic data analysis software package (http://www.brc.dcs.gla.ac.uk/systems/glama/), for maximal impact in the biological end-user community.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS
Health categories: GENERIC HEALTH RELEVANCE