Biochemsitry and signaling in disordered and crowded cells : a new space odyssey

Traditionally, the biochemical and signaling pathways within cells have been viewed by biologists as static objects with no space dependence. More recent experimental and modeling works in systems biology take into account their dynamics in time, but spatial aspects are still hardly investigated. Most often, they rely on mean-field equations ("laws of mass-action") that are strictly valid only if the reaction medium is dilute, perfectly-mixed and spatially homogeneous. Many of these assumptions may be violated in cells. Notably, single-cell measurements show that protein diffusion in most compartments (including cytosol, nucleus and membranes) is anomalous. This phenomenon is thought to be caused, at least in part, by physical obstruction to diffusion due to large-size obstacles, that actually abounds in cells (organelles, internal networks, large macromolecular complexes...). Fundamentally, these experimental observations tell us that cellular media can be considered as spatially inhomogeneous. To evaluate the effects of this inhomogeneity, we need to develop spatial cell biochemistry. In this talk, I will show that individual-based simulations can be used as a tool to understand several aspects of anomalous diffusion in cells and present examples of the effects it could have in the case of intracellular enzyme reaction and protein aggregation. Notably, the latter case is relevant to the study of aging in the bacterium Escherichia coli. Our simulations indeed suggests that molecular crowding plays a prominent role in this phenomenon. I will conclude with a brief presentation of our current effort towards hybrid simulation methods, i.e. simulation techniques mixing continuous mean-field modeling at the relevant length scale or spatial location, with discrete, individual-based simulations in other locations.

How can birds and bacteria move together without a leader? An introduction to collective motion in biology [SLIDES]

Collective motion in biology is observed at all scales, from herds and bird flocks, to bacterial swarms, and even down to a molecular level in the dynamics of actin and tubulin filaments. Often, the emerging, large-scale pattern of coherently moving entities exceeds by far the size of the constituent individuals. How can these individuals, e.g., bacteria, relying only on local information form large scale patterns? And how are these patterns? In order to answer these questions I will review and focus on the simplest conceivable mathematical model that exhibits collective motion: a system of particles where each particle attempts to copy the velocity direction of its neighbors. I will also show that these basic ideas can be actually applied to explain collective motion in a real biological system: myxobacteria.

Embryomorphic Engineering:
From biological development to self-organized computational architectures [SLIDES]

Traditional engineered ICT systems are qualitatively different from natural complex systems (CS). The former are made of unique, heterogeneous components assembled in complicated but precise ways, whereas the latter mostly rely on the repetition of agents following identical rules under stochastic dynamics. Thus, while natural CS often generate random patterns (spots, stripes, waves, trails, clusters, hubs, etc.), these patterns generally do not exhibit a true architecture like human-made ICT systems possess. Major exceptions, however, blur this dichotomy. (a) ICT-like CS: On the one hand, biology strikingly demonstrates the possibility of combining pure self-organization and elaborate architectures, such as the self-assembly of myriads of cells into the body plans of organisms, the synchronization of neural signals into mental states, or the stigmergic collaboration of swarms of social insects toward giant constructions. (b) CS-like ICT: Conversely, large-scale distributed ICT systems made of a multitude of components at all scales (integrated parts, software agents, network hosts) already exhibit complex emergent effects, albeit still mostly uncontrolled and unwanted. Thus, while some natural CS seemingly exhibit all the attributes of ICT systems, ICT systems are becoming natural objects of study for CS science. Such cross-boundary cases are examples of self-organized architectures—i.e., how spontaneous systems need not always be random and engineered systems need not always be directly designed—a hybrid concept insufficiently explored so far. I illustrate this goal with a multi-agent model of programmable and reproducible morphogenesis in a complex system. This model combines self-assembly (SA) and pattern formation (PF) under the control of a nonrandom gene regulatory network (GRN) stored inside each agent of the system. The differential properties of the agents (division, adhesion, migration) are determined by the regions of gene expression to which they belong, while at the same time these regions further expand and segment into subregions due to the self-assembly of differentiating agents. This model offers a new abstract framework, which I call Embryomorphic Engineering (coined after Neuromorphic Engineering), to explore the developmental link from genotype to phenotype that is needed in many emerging computational disciplines, such as 2-D/3-D collective robotics or n-D autonomic network topologies.

• Doursat R. (2009g) Facilitating evolutionary innovation by developmental modularity and variability. Generative & Developmental Systems Workshop (GDS 2009) at 18th Genetic and Evolutionary Computation Conference (GECCO 2009) July 8-12 2009 Montreal Canada.
  http://doursat.free.fr/docs/Doursat_2009_devo_GECCO.pdf
• Doursat R. & Ulieru M. (2008b) Emergent engineering for the management of complex situations. 2nd International Conference on Autonomic Computing and Communication Systems (Autonomics 2008) September 23-25 2008 Telecom Italia Labs Turin Italy.
  http://doursat.free.fr/docs/Doursat_Ulieru_2008_nets_Autonomics.pdf

Micro Motives and Macro Behaviour

The Schelling model of segregation on one and two dimensional lattices consists of a two color population, such that each person has a critical segregation threshold, i.e. the quantity of people of the other color in its neighborhood, so as to be unhappy. The dynamics consist of exchanging two unhappy people of different color. I'll prove that for some values of the unhappiness parameter there exists an energy which allows to understand the clusterisation of the space. Further, we analyse the model from the prediction point of view by asking the probability that one site changes color. We characterize for every parameter (1 up to 8 for the Moore neighborhood) whether or not the problem is P-Complete or belongs to the class NC.