22-24 Sep 2014 Nouméa (New Caledonia)
Dr. Pieter Collins graduated with a First Class Honours degree in Mathematics from Cambridge University in 1994, and obtained a Distinction in Part III of the Mathematics Tripos in 1995. He obtained a Ph.D. from the University of California at Berkeley in 1999, with a thesis entitled "Dynamics of Surface Maps with Homoclinic and Heteroclinic Tangles" under the supervision of Prof. Morris Hirsch. He subsequently held research positions at Liverpool University and the Centre for Mathematics and Computer Science (CWI) in Amsterdam, the latter supported by a five year "Vidi" grant from the Netherlands Organisation for Scientific Research (NWO) for the topic "Compuational Topology for Systems and Control". He is currently employed as a Lecturer (Universitair Docent) at the Department of Knowledge Engineering, Maastricht University.
Dr. Collins' research activities are concerned with rigorous numerical methods for the study of dynamic systems. His main interests from the viewpoint of systems biology are handling uncertainties in mathematical models of biological systems, and in simplifying models of complex systems to obtain models which are more more tractible for further study. He has participated in the European project EC-MoAn (Escherichia Coli - Modelling and Analysis), and is an advisor for the CyanoNet. He is currently supervision a Ph.D. student on a project involving mathematical modelling and analysis of the electrophysiology of cardiac myocytes.
Model-Checking in Systems Biology - From Micro to Macro
In this talk, I will describe what I see as the main challenges in the development of efficient, rigorous methods for model-checking the multiscale, highly uncertain dynamic systems arising in biological applications, and describe some theory and methods which address these. I will focus on two main aspects: 1) how we to handle modular and hierarchial systems, including abstraction of dynamics and system reduction, and 2) how to model uncertainties and handle the resulting models numerically. I will illustrate some approaches apply the tool ARIADNE for the rigorous analysis of dynamic systems to some models arising in cardiac electrophysiology.
Sašo Džeroski is a scientific councillor at the Jozef Stefan Institute and the Centre of Excellence for Integrated Approaches in Chemistry and Biology of Proteins, both in Ljubljana, Slovenia. He is also a professor at the Jozef Stefan International Postgraduate School. His research is mainly in the area of machine learning and data mining (including structured output prediction and automated modeling of dynamic systems) and their applications (mainly in environmental sciences, incl. ecology, and life sciences, incl. systems biology).
He has organized many scientific events, including the International Conference on Machine Learning and two recent workshops on Machine Learning in Systems Biology. He is co-author/co-editor of more than ten books/volumes, the latest two of which are »Computational Discovery of Scientific Knowledge« (2007) and »Inductive Databases and Constraint-Based Data Mining« (2010). He has participated in many international research projects (mostly EU-funded) and coordinated two of them, currently coordinating the FET XTrack project MAESTRA (Learning from Massive, Incompletely annotated, and Structured Data) and participating in the FET Flagship Human Brain Project.
Inductive Process Modeling for Learning the Dynamics of Biological Systems
Process-based models (PBMs) represent dynamic systems at two levels of abstraction. At the higher qualitative level, PBMs consist of entities and processes that describe the interactions between them. At the lower quantitative level, the models of individual processes provide details that allow the transformation of PBMs to systems of ODEs that can be used to precisely model the dynamics of the underlying system.
Inductive process modeling (IPM), a branch of computational scientific discovery, is concerned with the automated construction of PBMs from process-based modeling knowledge and measured data. The PB modeling knowledge describes classes of entities and processes in the modeling domain of discourse. This approach allows for constructing understandable PBMs, as well as the identification of both the structure and parameters of their underlying systems of ODEs.
IPM facilitates modular representation and reuse of domain knowledge. It has been successfully used to model biological systems at both the micro-level (i.e., to model cellular processes, such as endocytosis) and the macro-level (i.e., to model population dynamic processes in aquatic ecosystems, such as lakes and lagoons). We will introduce the task of inductive process modeling, describe some recent IPM approaches, and illustrate their use for modeling biological systems at both the micro- and macro- level.
Hugo Fort is a full Professor and Head of the Complex System Group at the Faculty of Sciences of the Republic University (Montevideo, Uruguay). His research interests evolved from quantum field theory to complex systems and mathematical modeling applied to problems in biology, with focus in ecology and evolution. Dr. Fort is currently involved in several projects covering from agronomic production (crop mixtures for overyielding, quantitative management for extensive livestock farm systems) to ecology (early warnings of catastrophic shifts, population dynamics, biodiversity patterns and conservation) and from evolution (quasispecies theory for RNA viral dynamics, e.coli evolutionary experiments) to game theory (the evolution of cooperation among self interested agents).
Hugo Fort earned his PhD in Physics from the Autonomous University of Barcelona in 1994. He was subsequently a Research Associate in the Institute for High Energy Physics, IFAE, in Catalonia, Spain where he conducted research on lattice gauge field theory.
Developing quantitative methods in community ecology: predicting species abundances from qualitative web interaction data
Quantitative predictions of biodiversity of human-impacted ecological communities are crucial for their management. In the case of plant–pollinator mutualistic networks, despite the great progress in describing the interactions between plants and their pollinators, the capability of making quantitative predictions is still in its infancy. Furthermore, a general problem is the lack of measures or estimations of species abundances.
Here I propose a general method to estimate pollinator species abundances and their niche distribution from the available data, namely network interaction matrices. It works by transforming a plant–pollinator network into a competition model between pollinator species. Competition matrices were obtained from ‘first principles’ calculations, using qualitative interaction matrices compiled for a set of 38 plant–pollinator networks. This method is able to make accurate quantitative predictions for mutualistic networks spanning a broad geographic range. Specifically, the predicted biodiversity metrics for pollinators – species relative abundances, Shannon equitability and Gini–Simpson indices – agree quite well with those inferred from empirical counts of visits of pollinators to plants. This method also allows building a one-dimensional niche axis for pollinators in which clusters of generalists are separated by specialists thus rendering support to the theory of emergent neutrality.
The importance of interspecific competition between pollinator species is a controversial and unresolved issue, considerable circumstantial evidence has accrued that competition between insects does occur, but a clear measure of its impact on their species abundances is still lacking. The present work contributed to fill this gap by quantifying the effect of competition between pollinators.
Particular applications could be to estimate the quantitative effects of removing a species from a community or to address the fate of populations of native organisms when foreign species are introduced to ecosystems far beyond their home range.
Vienna University of Technology & State University of New York at Stony Brook
Radu Grosu is a full Professor, and the Head of the Institute of Computer Engineering, at the Faculty of Informatics, of the Vienna University of Technology. He is also the Head of the Cyber-Physical-Systems Group within the Institute of Computer-Engineering, and a Research Professor at the Department of Computer Science, of the State University of New York at Stony Brook, USA.
The research interests of Radu Grosu include the modeling, the analysis and the control of cyber-physical systems and of biological systems. The applications focus of Radu Grosu includes distributed automotive and avionic systems, autonomous mobility, green operating systems, mobile ad-hoc networks, cardiac-cell networks, and genetic regulatory networks.
Radu Grosu is the recipient of the National Science Foundation Career Award, the State University of New York Research Foundation Promising Inventor Award, the Association for Computing Machinery Service Award, and is an elected member of the International Federation for Information Processing, Working Group 2.2.
Before receiving his appointment at the Vienna University of Technology, Radu Grosu was an Associate Professor in the Department of Computer Science, of the State University of New York at Stony Brook, where he co-directed the Concurrent-Systems Laboratory and co-founded the Systems-Biology Laboratory.
Radu Grosu earned his doctorate (Dr.rer.nat.) in Computer Science from the Faculty of Informatics of the Technical University München, Germany, and was subsequently a Research Associate in the Department of Computer and Information Science, of the University of Pennsylvania, USA, and an Assistant Professor in the Department of Computer Science, of the State University of New York at Stony Brook, USA.
Medical Cyber-Physical Systems: The Heart Challenge
This talk discusses the opportunities and research challenges faced in the modeling, analysis and control of the human heart. Consisting of more than 4 billion communication nodes, interconnected through a very sophisticated communication structure, this ultimate cyber-physical system achieves with an astonishing reliability, the electric synchronization and the mechanical contraction of all of its nodes, in order to pump blood, during what is commonly known as a heart beat. However, even this cyber-physical system, engineered by billion years of evolution is fallible. Predicting and controlling its failure is a great challenge for our society.
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
1992 - 1998: Study "Applied Systems Science" at the University Osnabrück; received my "Diplom-Systemwissenschaftler" degree in April 1998 May 1998: PhD student at the Max Planck Institute (MPI) for Dynamics of Complex Technical Systems (Systems Biology group) March 2005: PhD Thesis at University Stuttgart Since January 2009: Leader of Research Group "Analysis and Redesign of Biological Networks" at the MPI in Magdeburg
Fast Enumeration of Smallest Metabolic Engineering Strategies in Genome-Scale Networks
One ultimate goal of metabolic network modeling is the rational modification of biochemical networks to optimize the bio-based production of certain compounds. Although several constraint-based optimization techniques have been proposed for this purpose, there is still a need for computational approaches allowing an effective systematic enumeration of efficient intervention strategies in large-scale metabolic networks.
Here we present the MCSEnumerator approach by which a large number of the smallest genetic intervention strategies (with fewest targets) can be readily computed in genome-scale metabolic models . The algorithm builds upon an extended concept of Minimal Cut Sets (MCSs) which are minimal combinations of reaction (or gene) deletions leading to the fulfillment of a predefined intervention goal. It exploits the fact that smallest MCSs can be calculated as shortest elementary modes in a dual network and uses an improved procedure for shortest elementary-modes calculation. A broad spectrum of intervention problems can be formulated in a very convenient manner: one only needs to provide a description of the desired and undesired behaviors (flux distributions) by means of linear inequalities.
Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies for various intervention problems in genome-scale networks. We used MCSEnumerator to compute strain designs for growth-coupled synthesis of different products by heterotrophic as well as photoautotrophic organisms. We found numerous new engineering strategies partially requiring fewer interventions and guaranteeing higher product yields than reported previously.
In summary the presented approach can quickly calculate a large number of smallest metabolic engineering strategies with neither network size nor the number of required interventions posing major challenges. With its speed and the high flexibility in formulating intervention problems, we expect MCSEnumerator to become an important tool for Metabolic Engineering.
Moreover, the mathematical approach originally developed for finding interventions in biochemical reaction networks can be easily applied to any type of material flow networks, including those at macroscopic and ecosystems levels.
 von Kamp A and Klamt S (2014) Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Computational Biology 10: e1003378.
Pietro Lió is a Reader at the Computer Laboratory that is the department of Computer Science of the University of Cambridge. He has obtained one PhD in Complex Systems and Non Linear Dynamics (School of Informatics, dept of Engineering of the University of Firenze, Italy) and one PhD in (Theoretical) Genetics (University of Pavia, Italy). He teaches Bioinformatics algorithms and Biomedical Informatics courses in the same department, Mathematical modeling of comorbidities at the Centre for computational Biology in the department of Mathematics and in Systems Biology Tripos of the department of Biochemistry of the University of Cambridge. He is member of ACM and SMB (Society for Mathematical Biology). He is author of more than 250 papers in the field of computational biology and systems medicine. He has delivered twenty keynote talks at many international conferences and has organized several workshops and schools including a European Doctorate.
Ageing is perhaps related to the second law of thermodynamics due to the progressive decrease of our bodies' ability to expel entropy by cleaning metabolic debris and repairing the random damage occurring as a side-effect of metabolic activity. Environmental factors (macro scale biology), parental longevity and childhood are important predictors of exceptional longevity. There is a growing perception that aging will be amenable to medical intervention, although not as one intensive short time medication as we do with infections. Here we analyse molecular data (micro scale biology) from healthy controls of different age. Using these results and demographic variables from longevity studies we derive a mathematical model of ageing and related comorbidity maps.
IBENS, Ecole Normale Supérieure, 46 rue d’Ulm, 75005 Paris
After studying mathematics at the Ecole Normale Supérieure in Cachan, Hélène did a master in ecology in Paris. She then obtained a PhD in environmental sciences from the University of Bordeaux. After her PhD, she moved to the US where she stayed more than five years for two postdocs. During her first postdoc at the University of California, Merced and the University of Oregon, Eugene, her research focused on modelling macroecological patterns and developing approaches for studying microbial diversity. During her second postdoc at the University of Pennsylvania, Philadelphia and the University of California, Berkeley, her research focused on developing phylogenetic approaches for understanding diversification, long-term diversity dynamics, and community assembly. Hélène came back in France in October 2010 as a CNRS researcher, first at the Centre for Applied Mathematics at the Ecole Polytechnique, and now at the Institute of Biology at the Ecole Normale Supérieure. With her research group, she continues her interdisciplinary research at the interface between mathematics, bioinformatics, ecology and evolution.
Phylogenetic approaches for studying diversification
Estimating rates of speciation and extinction, and understanding how and why they vary over evolutionary time, geographical space and species groups, is a key to understanding how ecological and evolutionary processes generate biological diversity. Such inferences will increasingly benefit from phylogenetic approaches given the ever-accelerating rates of genetic sequencing. In the last few years, models designed to understand diversification from phylogenetic data have advanced significantly. I will review these approaches, focussing on the mathematical and computational challenges associated with each model. I will discuss way forwards and first results for developing models that more explicitly take into account ecology, in particular the interaction of species with each other and with their environment.
Daniel Stouffer is presently a Senior Lecturer in the School of Biological Sciences in the University of Canterbury, Christchurch, New Zealand. Before coming to New Zealand, Daniel completed a BS in Chemical Engineering at the Colorado School of Mines (USA) and a PhD in Chemical and Biological Engineering at Northwestern University (USA). While at Northwestern, Daniel's research interests evolved from complex systems in general to become largely ecological in focus. After completing his PhD, he was a postdoctoral fellow in the Resilience and Adaptive Management Group at the University of Alaska Anchorage (USA) before spending four years as a postdoctoral fellow at the Estación Biológica de Doñana (Spain). He eventually moved to Canterbury in mid-2011 were he started his own research group whose focus is the study of complexity in ecology. Throughout their research, they strive to apply tools from fields outside of biology, such as engineering or physics, to uncover patterns in empirical data and explore their ecological and evolutionary consequences.
Quantifying the complexity of “complex” ecological networks
Ecological networks are thought to be archetypal complex systems where the whole is far greater than the sum of its parts. Structurally, however, it remains unclear whether ecological networks are actually the product of unbridled complexity or if there are actually only a few ways to build an ecological network. In order to disentangle this problem, recent work has focused on developing new tools with which to quantify network complexity at a variety of scales—from the whole network down to individual species. Ultimately, such research will help us to better understand the way that species and species interactions are literally woven together in a larger network, and how that network acts in turn to place structural constraints on its constituent species.