Develop and apply a computational infrastructure to compile empirical knowledge and hypotheses about protein-protein interactions into models capable of revealing the molecular event structures leading up to specified observables, while easily keeping up with a rapidly changing knowledge base; quantify the aging phenotype at different levels of resolution, from molecular to organismal, and connect the phenotype with model-based reasoning.

Dr. Fontana is a leader in the development of highly sophisticated web-based modeling environment for the collaborative construction, maintenance, and modification of models. This environment works with a level of complexity that is not accessible to traditional techniques because of the astronomical number of potential molecular species that must be considered. The opportunities for drug target identification, synthetic biology, and molecular systems design are substantial. The Fontana Lab is interested in partnering, on an academic basis, with industrial sponsors to apply, test, and refine the web-based modeling environment, in the context of dynamic pathway discovery in multiple, overlapping, signaling networks that underlie the development and complex disease.

The Fontana Lab is interested in assessing the potential of optical scanners as scientific precision instruments, as well as in survival screens, related to the environmental, chemical, and genetic basis of aging in C.elegans. The Fontana lab has also collaborated with Dr. George Whitesides of Harvard University in developing a high throughput microfluidic device for C. elegans studies.
 

The theory group of the Fontana lab designs, utilizes, and refines a formal language for representing empirical facts, or hypotheses, about protein-protein interactions as executable rules. In this agent-event based paradigm, a complex molecular system is specified as a collection of rules that can be used to simulate the system’s dynamic behavior, as well as to reveal causal dependencies among rules or events. The lab deploys this framework in these collaborations:  the study of EGF, mTOR, and other large intracellular signaling systems; the exploration of emergent regulatory pathways and phenomena in overlapping networks; the distillation of kinetic and causal principles of molecular information processing based on transient complex formation and post-translational modification; and the development of evolutionary models aimed at understanding the evolvability of cellular decision networks.

The experimental section of the lab utilizes C.elegans to study the systems biology of aging. The lab devised an automated procedure for the acquisition of survival curves at high statistical and temporal resolution, enabling the high-throughput association of a complete lifespan distribution with any genotype or environmental condition. The lab is also developing techniques for the longitudinal study of molecular physiological states in single cells of individual organisms over their entire lifespans.

Dr. Fontana’s early career developed along these major areas of study:

1. The exploration of the interface between computer science and molecular biology. This work contributed to the establishment of an area of research now known as “artificial chemistry” that has applications in computer science and optimization.
2. The development of tools for the prediction of RNA secondary structure. These tools gave rise to a widely used public domain software package that has applications in structure prediction, the design of aptamers, and the indentification of microRNA targets.
3. The study of RNA sequences mapping to structures as a proxy for genotype-phenotype relations. The mappings are aimed at identifying statistical features that convey both robustness and evolvability.
These influential research studies have led to the concept of neutral networks and a different view of phenotype spaces.

When Dr. Fontana moved from a think-tank environment to the Harvard Medical School’s laboratory style of research, his computer science-molecular biology research matured into a new, and eminently practical, framework for modeling molecular signaling systems of staggering complexity. The Fontana laboratory added an experimental effort that focused on the systems biology of aging to characterize cross-sectional and longitudinal aging phenotypes associated with a specific set of genes.
 

Biological Mechanisms and Pathways

• Aging •
• Biological organization •
• Evolvability •
• Lifespan •
• Molecular information processing •
• Phosphorylation •
• Protein interactions •
• Signal transduction •
• Systems Biology
•  

Metabolism; Metabolic Disease and Aging

• Aging
•  

Research Tools and Instrumentation

• C. elegans •
• Computational modeling •
• High throughput •
• Interactive computer software •
• Molecular sensor
•  

Therapeutic Discovery Tools and Assays

• Computational modeling •
• High throughput
•