KDBIO group / INESC-ID and FCM/UNL

A challenge to nonlinear estimation: dynamic modeling of metabolic networks

Systems Biology is an emerging field that uses a global and integrative perspective to capture the behavior of complex living organisms. An important challenge in this area is the dynamic modeling, optimization and control of metabolic networks. A top-down approach can be conducted using experimental multivariate time series of metabolite concentrations, obtained through Nuclear Magnetic Resonance (NMR), and defining the model structure, given as a system of non-linear coupled differential equations. An important class of equations under Biochemical Systems Theory is commonly used, where rates are modeled with power-law functions. This methodology has proven to be flexible enough to be able to handle the most dissimilar metabolic networks and has the advantage that every single parameter can be directly interpreted biochemically, offering insight into the topological structure of the network and into the kinetic orders of the chemical reactions involved. However, the estimation of the parameters still constitutes a major difficulty, given the innumerous local minima and rough error surface, and is the bottleneck in the whole modeling process. As a case study, a preliminary model of glycolysis in Lactococcus lactis will be presented that could nevertheless provide important insights into the design of the pathway and the function of specific feedforward and feedback activations and inhibitions. Future work will be developed through project DyanMo, from the National Portuguese Science Foundation (FCT), with a multidisciplinary team constituted by INESC-ID, IST, ITQB-UNL, MDAnderson Cancer Center and Georgia Tech.

UC San Diego, USA

A New Approach to the Identification of Proteins and Post-translational Modifications

The ongoing success of the proteomics endeavor is the result of a prolific symbiosis between experimental ingenuity and efficient bioinformatics. But despite valuable contributions, the road to a better understanding of protein behavior is still hurdled by significant difficulties in the extensive identification of unexpected post-translational modifications and highly modified peptides. Recently, tandem mass spectrometry (MS/MS) based approaches seemed to be reaching the limit on the amount of information that could be extracted from MS/MS spectra. However, a closer look reveals that a common limiting procedure is to analyze each spectrum in isolation, even though high throughput mass spectrometry regularly generates many spectra from related peptides. By capitalizing on this redundancy we show that, similarly to the alignment of protein sequences, unidentified MS/MS spectra can also be aligned for the identification of modified and unmodified variants of the same peptide. Moreover, this alignment procedure can be iterated for the accurate grouping of multiple peptide variants. In fact, when applied to a set of spectra from cataractous lenses proteins from a 93-year old patient, spectral networks were able to capitalize on the highly correlated peaks in spectra from variants of the same peptide to rediscover the modifications identified by database search methods and additionally discovered several novel modification events.

TAGC (INSERM ERM 2006) http://tagc.univ-mrs.fr

Decision Diagrams for the Representation and Analysis of Logical Models of Genetic Networks

The complexity of biological regulatory networks calls for the development of proper mathematical methods to model their structures and to obtain insight in their dynamical behaviours. The generalised logical formalism is a qualitative approach consisting in modelling regulatory networks in terms of logical equations (using either Boolean or multi-valued discretisation).

We show that the use of Multi-valued Decision Diagrams enables the development of efficient algorithms for the analysis of specific dynamical properties of the regulatory graphs. In particular, we address the question of determining conditions insuring the functionality of feedback circuits, as well as the identification of stable states.

Finally, we apply these algorithms to logical models of T cell activation and differentiation.