At the molecular level, the cell is a crowded place and there are myriad ways that molecules collide and interact to provide the underpinnings for essential biochemical processes. A tangled web of biochemical reactions lies at the core of all cellular responses that guide each cell toward a specific fate. However, the number of molecules and their local microenvironment are in constant fluctuation from one cell to another, giving rise to multiple potential responses from each cell to the same signal. How is it that persistent cell-population behaviors emerge from biochemical processes that are governed by randomness (sometimes called "noise")? How do cells make such monumental decision of when to live and when to die in such a noisy environment? This work contributes to our understanding of this challenging question by exploring how noise in network-driven cellular processes can affect cellular commitment to fate. Specifically, we will explore how changes in the number of proteins, as well as intrinsic chemical reaction noise, contribute to different outcomes. A successful result from this work will lay the foundation to understand biochemical reaction networks as probabilistic rather than deterministic processes and enable us to develop novel theories to explain the role of noise in cell-population behaviors and their associated decision processes. This award will also provide significant resources to train the next generation of quantitative biologists who will gain experience at the interface of chemistry, physics, biology and computation.
As modern technologies are brought to bear at single-cell resolution, we are learning that stochasticity is ubiquitous feature in cellular processes. Despite evidence for non-genetic cell-response variability in cell populations, mechanistic interpretation of single-cell experiments typically appeal to a deterministic, non-existent "average cell", to describe network-driven biochemical mechanisms. Therefore, the role of stochastic molecular processes in biochemical networks and cellular commitment to fate is poorly understood. This shortcoming is not due to a lack of physicochemical theories to describe cellular processes, but rather to the computational and statistical challenges associated with the simulation of cellular network-driven processes subject to molecular noise, and the acquisition of data necessary to fully capture stochastic phenomena. The overarching goal of this award is to develop a mechanistic interpretation of cellular processes that can explain how molecular noise affects information flow in biochemical networks and predicts network-driven execution due to biochemical cues. To attain these goals, the work leverages high performance computing approaches, coupled with Bayesian statistics, and stochastic reaction kinetics to gain a foundational understanding of how noise impacts signal-processing in biochemical networks. Specifically, the work will explore how stochasticity from extrinsic (e.g. gene-expression) and intrinsic (e.g. complex formation) molecular sources contribute to cell-response variability and cell-population outcomes. The work focuses on apoptosis execution mechanisms, to explain non-genetic cellular heterogeneity in the response to programmed cell death cues. The work will also identify molecular sources that contribute to heterogeneous cellular response in apoptosis. The knowledge gained from this work could shift existing paradigms for our understanding of cellular commitment to fate, generalizable to other areas. The opportunities for training, dissemination, and collaboration afforded by this award will ensure that the work will have significant impact across multiple areas of biology as well as provide the environment to train the next generation of quantitative biologists.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.