| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
|
| Initial Amendment Date: | August 6, 2013 |
| Latest Amendment Date: | June 24, 2014 |
| Award Number: | 1318832 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Leland Jameson
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | August 15, 2013 |
| End Date: | July 31, 2017 (Estimated) |
| Total Intended Award Amount: | $249,999.00 |
| Total Awarded Amount to Date: | $249,999.00 |
| Funds Obligated to Date: |
FY 2014 = $80,913.00 |
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
21 N PARK ST STE 6301 MADISON WI US 53715-1218 (608)262-3822 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
21 North Park ST, STE 6401 Madison WI US 53715-1218 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): |
Cellular Dynamics and Function, COMPUTATIONAL MATHEMATICS, MSPA-INTERDISCIPLINARY |
| Primary Program Source: |
01001415DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
The objective of this research project is to develop and analyze next generation stochastic simulation methods for the models found in biochemistry. Such models include gene regulatory networks, neural networks, and models of viral infection and growth. Specifically, the two main research topics considered are the efficient computation of expectations and the efficient computation of parametric sensitivities. The mathematical focus of the project will the development of Monte Carlo estimators that are unbiased, yet orders of magnitude more efficient than the current state of the art. To achieve such efficiency, novel coupling procedures, sometimes used in conjunction with the multi-level Monte Carlo framework, will be employed in both project areas.
Due in part to the appearance of new technologies, most notably fluorescent proteins, there is now a large literature demonstrating that the fluctuations arising from the effective randomness of molecular interactions can have significant consequences, including a randomization of phenotypic outcomes and non-genetic population heterogeneity. In such cases, stochastic models, combined with both analytical and computational tools, are essential if they are to be well understood. The problems that will be addressed in this project often form the bottleneck in computational experiments in systems biology. Hence, the research will make possible many realistic modeling and simulation scenarios that are beyond the range of existing techniques. As the relevant models include those for both gene networks and viral growth, this project plays a role in improving long-term human health by greatly improving the predictive power of such models.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external
site maintained by the publisher. Some full text articles may not yet be available without a
charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from
this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This proposal concentrates on the computational challenges associated with some of the most common stochastic (random) models arising in cellular biology. If the abundances of the constituent molecules of a biological interaction network are sufficiently high then their concentrations are typically modeled by a coupled set of nonlinear ordinary differential equations. If, however, the abundances are low then the standard deterministic models do not provide a good representation of the behavior of the system and stochastic (random) models are used. Due in part to the appearance of new technologies, most notably fluorescent proteins, there is now a large literature demonstrating that the fluctuations arising from the effective randomness of molecular interactions can have significant consequences, including a randomization of phenotypic outcomes and nongenetic population heterogeneity. In such cases, stochastic models, combined with both analytical and computational tools, are essential if they are to be well understood.
This project focused on two well defined problems that form the bottleneck for many computational experiments in systems biology,
- Monte Carlo for expectations (i.e. computing relevant statistics for models), and
- Monte Carlo for parametric sensitivities (which determine how much the output of a system will change if certain subcomponents of that model are perturbed),
with the overarching goal of the proposal to make both theoretical advances and significant improvements in the efficiency and scope of this style of simulation.
The stated goals of the project were largely met. The results obtained under the support of this project will play a role in increasing our understanding of cellular processes, both through analytical results and by providing general purpose computational tools for biologists. Specifically, there have already been 16 papers, 2 textbooks, and 2 PhD thesis produced during the course of the grant, with more to come as projects that have already began finish up.
Last Modified: 10/24/2017
Modified by: David F Anderson
Please report errors in award information by writing to: awardsearch@nsf.gov.
