Tag Archive for: Physiome

j) Teaching

• Ongoing experimental projects
• Integrative research and modelling
• Instruments and techniques used
• k) Valorisation – Translational research
• Create clinical tools for differential diagnosis, following up of evolution of disease,2.2.2.2 Les questions

  Introduction

  Biological investigations provide knowledge and are expected, at some point, to translate into clinical research and medical advances for the treatment of human physiopathology. We hope to find cures for diseases and other key medical conditions, if possible, or at least to understand those conditions better. Yet it is increasingly clear that better understanding can only arise from a more holistic or integrative view of biological systems. We thus need to develop a better grasp of biological systems as complex systems, and to transfer this understanding into clinical research. Doing so requires a strongly interdisciplinary approach, and should provide novel insights into physiology and pathology.

  After a brief presentation of the general aims and concepts discussed in this topic, we list and offer details on four main challenges. How investigations should be driven in biology is a matter of debate. Should they be data-driven, object-driven or hypothesis-driven? Do we at least agree about the aim of deciphering the causal chains underlying biological processes? Do we expect models to bring insights and knowledge about the behaviour of biological systems, and to make accurate predictions?

  Recent advances in functional genomics and in the study of complex diseases (such as cancer, autoimmunity or infectious diseases, mitochondrial diseases or metabolic syndrome) have shown the necessity for an alternative way of thinking in biology, a view in which pathology and physiology result from interactions between many processes at different scales. The new scientific field of systems biology has emerged from this perspective; it focuses on the study of gene, protein, and biochemical reaction networks and cell population dynamics, considered as dynamical systems. It explores the biological properties resulting from the interaction of many components, investigating processes at different scales and their overall systemic integration. Complex systems science provides a conceptual framework and effective tools for unravelling emergent and immergent features from molecules to organisms and vice versa. The term “immergence” is meant to imply that some macro-level constraints cascade back in a causal way onto micro-levels. Both emergent and immergent properties should be understood from the multiscale reconstruction of data recorded at the appropriate

On each pathology

Treatment efficacy

Public conferences

spatial and temporal scales. We expect to find generic processes (design patterns for computer science) which apply from upper to lower levels of organization, and vice versa, and which allow their coupling e.g. synchronisation, reinforcement, amplification, inhibition, achieved through basic processes such as signalling through molecular interactions, diffusion, vesicular transport, ionic transport, electric coupling, biomechanical coupling and regulation of molecules and macromolecules characteristic features (including their concentrations).

Complex systems almost always involve a wide range of scales both in time (typically femtoseconds in chemical reactions, seconds in metabolism processes, days to months in cells, and years in an living organism) and space (typically nanometers for molecular structures, micrometers for supramolecular assemblies, organelles and cells, centimeters for tissues and organs, and meters for organisms). Finding the pertinent space and time scales for experimentation and modeling is a major issue. Classical approaches (biochemistry, cellular and molecular biology, behavioural and cognitive studies, etc.) usually have a “preferred” scale set by default, mainly due to the principle protocols and experiments being designed to work only at a specific scale. This makes back and forth interactions between different scales in observations, experimentations, models and simulations a very exciting transdisciplinary challenge.

Variation in biological systems raises the issue of an average, typical or representative behaviour. Determining such quantities, and knowing if they are scientifically useful,
requires characterizing and measuring variability and fluctuations at the molecular, single cell, cell population and physiological levels. The origin and functional significance of fluctuations in biological systems, even the scales of space and time on which they occur, remain largely unknown. Their functional significance might be approached through their multiscale transmission and possible amplification, reduction/damping or role in mediating bifurcations. Obviously, understanding will not arise from a one-to-one description and modeling of organisms (virtual cell, virtual organism) but rather from the correct identification of which components are relevant for a given problem and the reconstruction of models focused on the mechanisms involved. Such a reconstruction should use mathematical and physical tools, some borrowed from out-of-equilibrium thermodynamics and dynamical systems. New tools will also be required to answer specific questions of biology. Ultimately, injecting systemic vision and using complex systems principles and conceptual frameworks for a better understanding of human physio-pathology could lead to novel differential diagnosis and improve medical care.

Modern biology has in its development depended heavily on the notion of average behaviours and average individuals. But this conceptual framework has recently been challenged by empirical observation. Quantitative measurements of living single cells, or within such cells, have revealed extensive variability and fluctuation of cellular dynamics between different cells or between different times within the same cell. These observations open a new conceptual framework in biology, in which noise must be fully considered if we are to understand biological systems; this view departs from the classical framework which considered noise and fluctuations it mere measurement error or as “simple” thermodynamic fluctuations which should be suppressed by cells.

This new point of view raises many questions, as well as both practical and theoretical issues likely to deeply modify our understanding of biological systems. However, to tackle

these questions, we need to develop a complete scientific program of investigation ranging widely from precise measurements through to analysis of the origin and functional role of stochasticity in biological systems. Among the main breakthroughs, we need to:

· Improve the technology for quantitative measurements of noise and fluctuations in single cells, cell populations, tissues, organs and individuals. In particular, it will be necessary to identify the characteristic times at each level of organization and the most appropriate experimental indicators.

· Identify the mechanisms by which noise and fluctuations arise in biological systems. In particular, what are the modalities of multiscale transmission of fluctuations? Are fluctuations amplified or reduced/damped from one scale to the others? Are they important with respect to bifurcations in the organism/cell fate?

· Understand the functional significance of fluctuations in different biological systems. For instance, it has been proposed that fluctuations can enhance the robustness of living beings. However, other processes can be envisaged (e.g. stochastic resonance, increased signaling rates, cell differentiation, evolution, etc.). Such a functional significance supposes that biological systems are able to control the level of noise.

· Delineate possible mechanisms by which biological systems may control their level of fluctuation (negative/positive feedback loops in biochemical networks, neuronal adaptation in cortical networks, adaptive mutations and mutation hotspots, regulations and networks in the immune system).

· Question the meaning of usual averaging processes in experimental biology. In the case of biochemical networks, can data gathered on cell populations be used to infer the actual network in a given single cell? Similar issues arise in the case of connectivity structures of cortical networks and cell lineage reconstruction.

These issues can be addressed in various biological systems including (but not limited to):

· Transcription and regulation networks: it is now clear that the transcriptional activity of the cell is highly stochastic. Some of the molecular causes of this stochasticity have been identified, yet its precise origin and regulatory mechanisms remain to be discovered. Doing so will first require the development of adequate measurement methodologies to enable us to quantify these fluctuations at different time scales in single cells.

· Neurons and neuronal networks: the so-called “on-going” activity within cortical circuits is a spontaneous activity generated by the recurrent nature of these networks. It has long been considered a mere noise added to the environmental signals. However, more recent studies have proposed a real functional role in which ongoing activity could facilitate signal spreading and be implicated in adaptive processes. Inhibitory effects have been shown to reduce variability at both the single-cell and population level.

· Diversity of the immune system: The immune system is characterized by diversity at different levels. Lymphocyte receptor diversity, populations of effectors and regulators, cell-population dynamics, cell selection and competition, and migration

through the whole organism are the result of stochastic or selection mechanisms whose impact on the overall efficiency of the system needs to be further characterized.

· Uncontrolled variability is often accused of being a source of major perturbations in the fate of organisms. Examples can be found in the process of aging, cancer, autoimmunity, infections or degenerative diseases. Yet the precise influence of noise is still open to debate. In particular, one point is to determine to what extent degenerative processes are a consequence of noise accumulation, a variation in noise properties or of rare stochastic events.

· Variability at the genetic level is the major engine of evolution. But genetic variability may be indirectly regulated according to the spatio-temporal characteristics of the environment (selection for robustness, for example, or for evolvability).

Moreover, clonal individuals may be very different from each other due to intrinsic and extrinsic phenotypic variability. The mechanisms by which heritable and non-heritable variability are regulated still need to be characterized and their influence on the
evolutionary process is largely unknown.

Concerning the modeling of fluctuations, several mathematical and physical tools
exist, but these need to be improved. Thus, stochastic models are largely used in molecular systems biology. The simulation algorithms (Gillespie algorithm) use the Delbrück-Bartholomay-Rényi representation of biochemical kinetics as jump Markov processes. In order to improve the performance of these methods (which are costly in time) several approximate schemes
have been proposed, for instance the approximation of Poisson variables by Gaussians
(tau-leap method). Hybrid approximations are more appropriate when the processes are
multiscale and these approximations could be developed by combining averaging and
the law of large numbers. In certain simple cases, the master equation can be exactly
solved.

It is also interesting to transfer ideas from statistical physics to biology. For instance, fluctuation theorems, which concern the occurrence of out-of-equilibrium fluctuations in heat exchanges with the surrounding environment and work theorems, concerning thermodynamic fluctuations in small systems close to equilibrium, could be applied to characterize fluctuations in gene networks, DNA transcription processes and the unfolding of biomolecules.

b) Stability in biology

We encounter various definitions of stability depending on the phenomenon, the model or the community proposing the concept. Frequently invoked concepts include homeostasis in relation to metabolic control, the Red Queen concept in evolution describing continuous development to sustain stable fitness in a changing environment, robustness in systems biology referring to insensitivity with respect to perturbations, or canalization and attractors in developmental biology and ecology.

The main challenges are:

In seeking to understand the stability of biological systems, which are always subject to both intrinsic and extrinsic perturbations, we need to develop the notion of steady state, or

more generally attractor. We need new mathematical concepts to capture the subtleties of biological stability.

Finite-time stability is a concept that can be used to define stability in the case when the system is known to operate or to preserve its structure unchanged over a finite
time. We are interested in the conditions under which the system’s variables remain
within finite bounds. Can we extend such formalism to other properties (oscillations, optimal biomass production, etc.)?

Finite time stability depends on the existence of subsystems with different relaxation times. It is thus important to develop methods allowing to estimate the largest
relaxation time of subsystems. For compound systems, how can we relate the
relaxation times of the elements to that of the system?

The notion of resilience is also a generalization of stability that is particularly appealing in this context. Indeed, it focuses on the ability to restore or maintain important functions when submitted to perturbations. The formalizations of this concept, founded on dynamical system properties (measure of attraction basin sizes), or even on viability theory (cost to return into a viability kernel) should become more operational to favour a wider diffusion.

The functioning of multicellular organisms occurs at the level of the cellular population, not of the individual cell. Furthermore, the stability of a cell population (tissue) is generally different from that of the individual cell. Cells extracted from tumours, for example, can reverse to normal activity when injected into healthy tissue. In this context, how can we define and study the stability of a population in relation to the stability of individuals? In addition, the same relation should be considered in the context of a developing organism taking into account differentiation and organogenesis. These processes are examples of symmetry-breaking, and we would like to determine whether symmetry arguments can be used in the study of stability properties.

Systems biology studies robustness as an important organizing principle of biological systems. As pointed out by H. Kitano, cancer is a robust system with some points of fragility. Thus, finding treatments and cures for diseases may consist in determining the fragility points of a robust system. In order to answer this question, we need good models, new mathematical theories and computer tools to analyse properties of models and new experimental techniques to quantify robustness.

Complexity and stability. In the modeling process, we should be able to zoom in and out between various levels of complexity. Stable properties of the system could be those that are common to several levels of complexity. More generally, is there a connexion between stability and complexity?

c) Multiscaling

Biological processes involve events and processes taking place over many different
scales of time and space. A hierarchical relationship among these scales enters our description only because it corresponds to our subjective views, usually based on our limited
experimental access to the system. Multiscale approaches drawn from theoretical physics
have been developed essentially in an unidirectional (bottom-up) way, to integrate parameters and

mechanisms at a given scale into effective, and hopefully reduced, descriptions at higher scales. However, lower-scale properties are directly coupled with properties of the higher scales (e.g. 3D chromosome distribution in the nucleus partly governs gene expression, which itself participates in nuclear architecture). The very complexity of living systems and biological functions lies partly in the presence of these bidirectional feedbacks between higher.

References

Baudrit, C., Sicard, M., Wuillemin, P.H., Perrot N. (2010). Towards a global modelling of the Camembert-type cheese ripening process by coupling heterogeneous knowledge with dynamic Bayesian networks, Journal of Food Engineering, 98 (3), 283-293.

Baudrit, C., Hélias, A., & Perrot, N. (2009). A Joint treatment of imprecision and variability in food engineering: Application to cheese mass loss during ripening. Journal of Food Engineering, 93, 284-292.

Barrière, O., Lutton, E., Baudrit, C., Sicard, M., Pinaud B., Perrot, N. (2008). Modeling Human Expertise on a Cheese Ripening Industrial Process Using GP. Lecture Notes in Computer Science, Parallel Problem Solving from Nature – PPSN X , Springer Berlin/Heidelberg (Eds). Vol 5199, pp. 859-868.

Baudrit, C., Wuillemin, P.H., Sicard M., Perrot, N. (2008). A Dynamic Bayesian Networkto Represent a Ripening Process of a Soft Mould Cheese. Lecture Notes in
Computer Science, Knowledge-Based Intelligent Information and Engineering
Systems. Springer Berlin/Heidelberg (Eds). Vol 5178, pp. 265-272.

Baudrit, C., Dubois, D., Perrot, N. (2008). Representing parametric probabilistic models tainted with imprecision Fuzzy Sets and Systems, Vol 159, Issue 15, pp.
1913-1928.

Allais, I., Perrot, N., Curt, C., Trystram, G., (2007) “Modelling the operator know-how to control sensory quality in traditional processes in Journal of Food engineering. 83 (2): 156-166.

Perrot, N. (2006). Fuzzy concepts applied to food product quality control. Editorial. Fuzzy Sets and Systems, 157, 1143-1144.

Perrot, N., Ioannou, I., Allais, I., Curt, C., Hossenlopp, J., Trystram, G. (2006). Fuzzy concepts applied to food product quality control: a review. Fuzzy Sets and Systems, 157, 1145-1154.

Ioannou, I., Mauris, G., Trystram, G., Perrot, N. (2006). Back-propagation of imprecision in a cheese ripening fuzzy model based on human sensory evaluations. Fuzzy Sets and Systems, 157, 1179-1187.

Articles dans congrès internationaux avec actes:

Baudrit, C., Lamrini, B., Sicard, M., Wuillemin, P.H., *Perrot*, N (2009). Dynamic neural networks versus dynamic Bayesian networks to model microbial behaviours during the cheese ripening process. /European Federation of Food Science and Technology Conference (EFFoST)/, Budapest, Hungary.

Lamrini B., *Perrot* N., Della Valle G., Chiron H., Trelea I.C. and Trystram G. (2009). A dynamic neural model to describe a leavening of bread dough Submitted in /European Federation of Food Science and Technology Conference (EFFoST)/, Budapest, Hungary.

Sicard M., *Perrot* N., Baudrit C.,Reuillon R., Bourgine P., Alvarez I., Martin S. (2009). The viability theory to control complex food processes./ European Conference on Complex Systems (ECCS’09)/,* *University of Warwick (UK).

Baudrit, C., Sicard, M., Wuillemin, P.H., *Perrot*, N. (2009). „Dynamic Bayesian Networks for Modelling Food Processing: Application to the Cheese Ripening Process”. /8th World Congress of Chemical Engineering (WCCE8),/ Montréal (Canada), 2009.

Baudrit, C., Sicard, M., Wuillemin, P.H., *Perrot, *N. (2009).”Toward a knowledge integration for representing food processes”. /European Conference on Complex Systems (ECCS’09)/, University of Warwick (UK), 2009.

Pinaud, B., Baudrit, C., Sicard, M., Wuillemin, P.H., Perrot, N. (2008). Validation et enrichissement interactifs d’un apprentissage automatique des paramètres d’un RBD. 4ème Journées Francophones sur les Réseaux Bayésiens. Lyon, 29-30 th May.

Sicard, M;, Leclerc-Perlat, M.N., Perrot, N. (2008) “Knowledge Integration for Cheese Ripening Following”, 5th IDF symposium on cheese ripening Bern Switzerland 9-13 March 2008.

Baudrit, C., Hélias, A., Perrot, N. (2007) Uncertainty analysis in food engineering involving imprecision and randomness. ISIPTA’07 – Fifth International Symposium on Imprecise Probability: Theories and Applications, Prague, Czech Republic, pp 21-30, 2007. http://www.sipta.org/isipta07/proceedings/027.html

Perrot, N., Allais, I., Edoura Gaena R.B., Ioannou, I., Trystram, G., Mauris, G. (2006) A methodological guideline for the expert-operator knowledge management in the food industry. In proceedings of the Foodsim congress, Naples, 15-17 June, Italie,195-200.