Seminars & Events
Mathematics for Systems Biology Seminar Series
"Emergence in Complex Systems"
DATE: February 22, 2007
TIME: 1:00pm to 5:00pm
LOCATION: Computation Institute, RI 480, University of Chicago
Description:
This seminar will focus on the phenomenon of Emergence in Complex Systems.
In 1999 Jeffrey Goldstein defined emergence as "the arising of novel and coherent structures, patterns and properties during the process of self-organization in complex systems". An emergent behavior appears when a number of simple entities (e.g. cellular components) operate in an environment, forming more complex behaviors as a collective. Emergence occurring over disparate size scales is usually based on causal relations across different scales formed by top-down feedback in the systems with emergent properties.
The study of emergence in biology provides valuable information about the organization of cellular processes. Oscillations are the simplest case of emergent coherence found in complex systems. They can emerge as the collective dynamic behavior of an ensemble of interacting components
in the cell. Genetic oscillators form the basis of circadian clocks [A. Winfree, 2002]. The key characteristics of this dynamic state can be quantitatively determined in experiments.
The emergence of oscillations in a complex system is subtle as it depends crucially on the dynamic properties of the interacting components and their collective behaviors.
The seminar will feature three invited presentations:
- Prof. Michael Berry, Physics Department, Bristol University
Emergence and Asymptotics in Physics: How One Theory Can Live inside Another
In physics, each deeper level of description (e.g., quantum mechanics) "reduces" to a less general level (e.g., Newtonian mechanics) in the asymptotic limit when a characteristic parameter (e.g., Planck’s constant) is negligibly small. Such limits are usually singular; hence, the different levels can involve very different concepts ("incommensurability") and lead to predictions of qualitatively new phenomena inhabiting the borderlands between theories. Thus, the philosophical problem of theory reduction can itself be reduced &em; in physics at least – to technical problems in mathematical asymptotics.
- Prof. Evgeni Selkov, Encyclopedia Genomica, Austria; the University of Chicago
Metabolic Incompatibility and Square Waveform of the Cell Clock
The metabolic networks of all living organisms have many potentially incompatible processes. Synthesis and simultaneous degradation of nucleic acids, proteins, reserved compounds, photosynthetic oxygen evolution and nitrogen fixation are examples of such incompatibility. Mathematical analysis predicts that the incompatibility makes normal functioning of the metabolism impossible. In particular, the intermediary cell energy metabolism cannot generate net ATP (or any of its equivalents) under a steady state owing to the strong futile recycling of intermediates. Periodic temporal separation of incompatible processes is the only way of suppressing the incompatibility. Such separation requires periodic control signals generated by a metabolic self-oscillator, a cell clock. If not dictated by the adaptation to the diurnal environmental changes, the clock must not be necessarily a circadian one.
Theoretically, oscillations generated by the clock (whatever its mechanism) must have a square waveform to minimize overlapping of incompatible processes during the transitions between alternative metabolic states. Such a waveform cannot be generated by a negative feedback mechanism with or without gene expression control. It can be generated only by a strong and fast positive feedback. To verify this critical theoretical prediction, we used chemostat cultures of cyanobactera. The experimental setup allowed a stabile autosynchronous growth of the cells during many weeks and continuous sampling and processing of metabolic activity with sensors of O_2 , CO_2 , pH, and redox potential. The recorded data confirmed the existence of a perfect square waveform in the cyanobacterial circadian temporal organization. This waveform persisted in the cells with completely blocked protein synthesis.
- Prof. Orly Alter, University of Texas at Austin
Discovery of Principles of Nature from Mathematical Modeling of DNA Microarray Data
DNA microarrays make it possible to record the complete genomic signals that guide the progression of cellular processes. Future predictive power, discovery, and control in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment, and drug development. I will describe the first data-driven models created from these large-scale data through generalizations of matrix and tensor computations that have proven successful in describing the physical world. In these models, the mathematical variables and operations might represent biological reality: The variables, patterns uncovered in the data, might correlate with activities of cellular elements, such as regulators or transcription factors, that drive the measured signals. The operations, such as data classification and reconstruction in subspaces of selected patterns, might simulate experimental observation of the correlations and possibly also causal coordination of these activities.
I will illustrate these models in comparative and integrative analyses of mRNA expression and proteins’ DNA-binding data from yeast and human cell cultures. In these analyses, the ability of the models to predict previously unknown biological and physical principles is demonstrated with a prediction of a novel mechanism of regulation that correlates DNA replication initiation with RNA transcription. The predicted mechanism is in agreement with current biological understanding and is supported by recent experimental results.
I will also illustrate the models in the analysis of yeast genome-scale mRNA lengths distribution data measured with DNA microarrays. SVD uncovers in thse data "asymmetric Hermite functions," a generalization of the eigenfunction of the quantum harmonic oscillator. These patterns of mRNA abundance levels across gel migration lengths might be explained by a previously undiscovered asymmetry in RNA gel electrophoresis thermal band broadening. These models may become the foundation of a future in which biological systems are modeled and controlled as physical systems are today.
More Information:
Sponsored by:
Joint Theory Institute (Argonne National Laboratory, University of Chicago)
Computation Institute (Argonne National Laboratory, University of Chicago)
Mathematics and Computer Science Division, Argonne National Laboratory
We encourage the audience to participate in an open discussion. Three abstracts describing problems relevant to the topic of emergence in biology will be selected for a 10-minute informal presentation and a panel discussion. If you are interested in making such a presentation, please submit a short abstract to Natalia Maltsev
(maltsev@mcs.anl.gov) by February, 19.
more info >>
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