National Science Foundation     |     Directorate for Engineering  (ENG)
Division of Chemical, Bioengineering, Environmental, & Transport Systems  (CBET)
 
CBET Research Highlights
Notable Accomplishments from CBET Awards
 
 
"How the Biological Clock Works"
provides a testing ground for the new discipline of Systems Biology

 
Jonathan Arnold & Heinz-Bernd Schüttler  –  University of Georgia
David Logan  –  Clark Atlanta University

Background:  Biological clocks are ubiquitous and found in bacteria to humans.  They permit an organism to adapt to the light-dark cycle of the planet, affecting when plankton move in the water column, when plants open/orient their leaves, open their stomata (pores in stems and leaves for gas and water exchange), and grow, when dinoflagellates (e.g., marine plankton) communicate by their bioluminescence nightly, when small mammals emerge to feed to avoid predators, and when many organisms reproduce (Figure 1).  The biological clock has profound effects on human health and longevity with connections to cancer and sleep disorders on the one hand and to heart and lung disease on the other hand.  Recent biotechnological interest in biological clocks focuses on exploiting the timed delivery of medicine and other biologicals in plants/fungi and mammals.
 
Results:  In 2007, the NSF-funded Arnold and Schüttler team developed the first working model of the biological clock (PNAS USA 104: 2809) by employing a new ensemble method for identifying a biochemical and regulatory network or genetic network (for short) from genomics experiments.  This breakthrough relied on using ensemble methodologies first developed in statistical physics in the 19th century by Boltzmann for understanding gases and liquids where only a limited number of observations (pressure, volume, and temperature) are available.  With this ensemble approach, the team was able to overcome the general problem of identifying an underlying kinetic model of the clock with many parameters from limited experimental data.  The working model captured two essential features of the clock, its entrainment to light and dark (Figure 1) and the circadian rhythm.
 
Jonathan Arnold Image 1

Figure 1.  The biological clock is remarkably adaptive in its light entrainment to varied artificial days.  Replicate race tubes are inoculated at one end and subject to a 6 hr, 18 hr, and 48 hr artificial day over 7 ordinary days.  The clock is manifested by the appearance of orange bands (i.e., asexual production of spores) as the culture grows to the other end of the tube.  In each artificial day the race tubes experienced:
(A) 3 hrs light and 3 hrs dark,         (B) 9 hrs light and 9 hrs dark, or         (C) 24 hrs light and 24 hrs dark.

In 2008, the team solved one of the major problems in systems biology (Science 295: 1662): how to use a model-guided discovery process to select the maximally informative next experiment about a genetic network in a sequence of very expensive genomics experiments (PLoS ONE 3(8): e3105, doi:10.1371/journal.pone.0003105).  The outcome of developing this process, called Computing Life (Figure 2), deployed the working model ensemble of the clock to guide genomics experiments to the identification of most genes under the direct control of the clock in a major model fungal system, Neurospora crassa.  An unexpected finding from this enactment of this Computing Life paradigm was that the biological clock controls ribosome biogenesis, the process of making ribosomes, the protein factories of the cell.  This may help to explain why so many (25 percent) genes in the N. crassa genome were found to be circadian in their rhythm (IET Systems Biology 1 (No 5): 257-265, 2007) by virtue of ribosome biogenesis touching so many other processes going on in the cell.
 
The methodologies of ensemble identification and Computing Life developed here and used successfully to elucidate the molecular mechanisms of the biological clock are generally applicable to large-scale biological networks in cells controlling diverse processes from development to signaling.  The team is testing these methodologies on one other early paradigm for eukaryotic gene regulation with very different kinetics, the qa gene cluster in N. crassa (Bioinformation 1(10): 390-395, 2006) with Dr. David Logan at Clark Atlanta University.  The team is beginning to make these tools publicly available on the Web (Computers and Mathematics with Applications, 2008, doi:10.1016/j.camwa.2008.10.086)

Jonathan Arnold Image 2
 
Figure 2.  Computing Life Paradigm.

 
Credit for Figures 1 & 2:  Systems Biology of the Clock in Neurospora crassa article - by Jonathan Arnold, H. Bernd Schüttler, et.al - - PLoS ONE 3: e3105, 2008 [PLoS ONE is an open access, online scientific journal from the Public Library of Science (ISSN 1817-101X)]
 
This project addresses the NSF Strategic Outcome Goals, as described in the NSF Strategic Plan 2006-2011, as follows:
 
Primary Strategic Outcome Goal:       (1) Discovery:  NSF emphasizes investigations that are cross-disciplinary and require a systems approach to address complex problems.  This proposal is about developing new tools in systems biology, the ensemble method drawn from Statistical Physics and the Computing Life paradigm, to understand the complex problem of how organisms keep time.
 
This research team has developed a model-driven discovery cycle called Computing Life to identify genetic networks describing fundamental biological processes, such as the biological clock.
 
                                                                    (1) Discovery Categories:
                                                                           - International Collaborative Research
                                                                           - Biological Sciences
                                                                           - Computer & Information Science and Engineering
                                                                           - Cyberinfrastructure, excluding Shared Cyberinfrastructure Tools
                                                                           - Engineering Research
                                                                           - Mathematical & Physical Sciences
 
Secondary Strategic Outcome Goal:  (2) Learning:  The research team has:
 
* Introduced new systems biology tools and their applications into an NSF REU (Research Experience for Undergraduates) site for under-represented groups.  Undergraduates contributed to all results listed above.  This work is in partnership with Clark Atlanta University with Dr. David A. Logan's support.
 
* Trained 2 graduate students in enacting research in the new area of systems biology, providing them dual training in genomics and computational biology.
 
* Trained 1 postdoctoral fellow in research in systems biology, providing dual training in genomics and computational biology and to prepare him/her for a faculty position in this area.
 
* Developed the career of one senior professor, Dr. David Logan at Clark Atlanta University, in systems biology of the qa gene cluster in N. crassa.
 
* Supported science educator, Dr. J. Steve Oliver at the University of Georgia, in developing new curricula for K-12.
 
* Developed Web-based simulator for genetic networks for incorporation into varied curricula at all levels.
 
* Co-organized first international meeting on computational systems biology (2006, Shanghai) in partnership with Dr. Momaiio Xiong.
 
Two Ph.D. students and one Masters degree student will have obtained their degrees under this grant, and one postdoctoral fellow has been trained and is searching for a faculty position now.  Dr. Logan has been supported in his career development and been productive in publishing in systems biology.  Dr. Oliver has been supported in K-12 curriculum development.  Materials and methods, such as a genetic network simulator (Computers and Mathematics with Applications, 2008, doi:10.1016/j.camwa.2008.10.086) were incorporated into undergraduate and graduate courses in Bioinformatics and Genomics at both the University of Georgia and Clark Atlanta University.  The research team helped to organize the First International Conference on Computational Systems Biology, 2006, Fudan University, Shanghai, China.  A Conference proceedings was published in a special issue of IET Systems Biology 1 (No 5) (2007).
 
                                                                    (2) Learning Categories:
                                                                           - K-12 Education
                                                                           - Teacher Education and In-service Professional Development
                                                                           - Undergraduate Education and Undergraduate Student Research
                                                                           - Graduate Education and Graduate Student Research
                                                                           - Postdoctoral Education, including International Postdoctoral Fellowships
                                                                           - Broadening Participation to Improve Workforce Development
 
Secondary Strategic Outcome Goal:  (3) Research Infrastructure:  NSF also wishes to develop new tools in computational science and engineering to drive discovery.  This is a fundamental problem in systems biology.  Work supported by this grant for the first time provides a solution to this problem for very costly genomics experiments without shortcuts (such as assuming steady state or the system being linear).
 
The team has developed a new engineering methodology that allows their working model of the biological clock to guide the experiments to discoveries about the clock, such as its connection to ribosome biogenesis.  The cycle of modeling and experiments are integrated into one framework called Computing Life and enacted to understand the biological clock.
 
                                                                    (3) Research Infrastructure Categories:
                                                                           - Shared Cyberinfrastructure Tools
                                                                           - Other Infrastructure and Research Resources

Scientific Uniqueness:  For the first time the team has overcome a common problem in identifying molecular models for how cells work: the kinetic models have many parameters, but limited data to identify and evaluate them.  The team successfully used an ensemble approach from statistical physics to identify a working model of the biological clock for the first time in the model fungal system, N. crassa, in which many discoveries about the biological clock have originated.  A second major outstanding problem of systems biology is: how can these molecular models be used to guide the discovery process by choosing rationally the next very expensive genomics experiment in a sequence of such experiments?  The team has solved this problem generally for the first time with a process called Computing Life and enacted this process to identify most clock-controlled genes in N. crassa and to discover an unexpected connection between the clock and ribosome biogenesis.

In terms of Intellectual Merit, this outcome is notable.  NSF and the scientific community have long sought approaches for rationalized discovery.  It began with machine computing, which led to machine optimization and learning.  It is natural to ask for a process that takes the next step in science, machine discovery.  Remarkable progress has been made on related engineering problems.  Chemical engineers can optimize a chemical production process.  Statisticians have developed tools for optimal designs to tune an industrial process.  Electrical engineers have developed methodologies for adaptive control.
 
The problem of rationalized scientific discovery, however, is distinct and of equally high intellectual merit, particularly in a living system.  The chemical reaction networks are very large, but the genomics experiments measuring the cellular state are quite limited and costly.  Each cycle of experiments typically costs $250,000.  The goal is also distinct; it is not one of control or process identification, but the question is how to choose rationally the next experiment to yield the most new information about the unknown process in the cell.  This is the problem that the Arnold-Schüttler team solved for the first time, and the team enacted the solution on a fundamental process, how the cell keeps time.

In terms of Broader Impacts, this outcome is notable.  For the first time, the engineering tools of Computing Life are developed so that an ensemble of molecular models for a particular cellular process (i.e., an ensemble of genetic networks) guides the enactment of a costly sequence of genomics experiments to new discoveries about a fundamental biological process, the biological clock.  The resulting discovery is that the biological clock is controlling ribosome biogenesis.  This connection may help to explain why so many processes in the cell are affected by the biological clock.  The engineering tools used to make this discovery are general and can be applied to many other cellular processes.  The development of these tools can have a broad impact on achieving new discoveries about cellular processes in an automated fashion.

This research is Transformative, and for the first time allows a new engineering approach - - in which a molecular model guides several cycles of genomics experiments to discoveries about a fundamental biological process.  The methodology was enacted for the fundamental process of the biological clock, allowing the discovery of most clock-controlled genes and an unexpected connection between the clock and how the cell assembles the protein factories of the cell, the ribosomes.

This research represents Broadening Participation.  The engineering tools for this project were used to support an NSF REU site for under-represented groups, and REU participants participated in all aspects of this project.  All but one of the papers listed above have undergraduate REU participants as co-authors.  This project was carried out in partnership with Dr. David A. Logan at Clark Atlanta University and also supported a career development award to Dr. Logan to allow him to move into the new and exciting area of systems biology (Bioinformation 1(10): 390-395, 2006; IET Systems Biology 1 (No 5): 257-265, 2007).

Existing or potential Societal Benefits of this research:  The biological clock is a process that affects human health from sleep disorders and cancer to heart and lung disease.  The clock has been linked to human longevity as well.  The teams have achieved a fundamental understanding of the molecular mechanism of the clock with tools developed here that will allow us to understand how the clock is connected to human health and disease.
 
More generally, the engineering methodology, Computing Life, developed here could be applied to a wide range of biological processes, including cell signaling, which is targeted by many pharmaceuticals.  The Computing Life methodology could also be applied to the engineering of biological systems of economic interest, such as those involved in ethanol production.  Finally, by achieving a detailed understanding of how the biological clock works with the Computing Life methodology, the teams are now in a position to develop vehicles for the timed-delivery of biologicals to plant and mammalian systems.


 
Program Director:
 
 
 
Fred Heineken
CBET Program Director - Biotechnology, Biochemical, and Biomass Engineering
     
NSF Award Numbers:   0425762, 0542915, 0646315
     
Award Titles:
 
 
  (1) QSB: Computing Life and the Kinetics of the Cell
(2) CAA: Identification of Genetic Networks in Neurospora Crassa: A Systems Biology Approach
(1) REU Site: Genomics and Computational Biology
     
PI Names:
 
 
  (1) Jonathan Arnold
(1) Heinz-Bernd Schüttler
(2) David Logan
     
Institution Names:   (1) University of Georgia
(2) Clark Atlanta University
     
Program Element Codes:   1491, 7454, 1112, 7487, T787, T370, 1139
     
NSF Investments:
 
 
 
 
  - American Competitiveness Initiative (ACI)
- Cyber-enabled Discovery and Innovation (CDI)
- Understanding Complex Biological Systems (including the
      interfaces of life, physical, and computational sciences)
- Adaptive Systems Technology
     
CBET Research Highlight:

  FY 2009


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This Research Highlight was Updated on 5 December 2012.