2011 Annual Science Report
Massachusetts Institute of Technology Reporting | SEP 2010 – AUG 2011
Metabolic Networks From Single Cells to Ecosystems
Members of the Segre’ group use systems biology approaches to study the complex network of metabolic reactions that allow microbial cells to survive and reproduce under varying environmental conditions. The resource allocation problem that underlies these fundamental processes changes dramatically when multiple cells can compete or cooperate with each other, for example through metabolic cross-feeding. Through mathematical models of microbial ecosystems and computer simulations of spatially structured cell populations, the Segre’ team aims at understanding the environmental conditions and evolutionary processes that favor the emergence of multicellular organization in living systems.
Cross-feeding, oxygen-dependence and spatio-temporal dynamics in microbial ecosystems
The metabolic capabilities of many important microbes can be quantitatively explored using systems biology approaches to metabolic networks. Yet, as we learn more about the complex microbe-microbe and microbe-environment interactions in microbial communities, it is important to understand whether and how system-level approaches can be extended to the multi-cellular and ecosystem level. We have been addressing these challenges at multiple scales, starting from two-species natural and synthetic ecology models, up to biosphere-level approaches. Recent developments include (i) the generation of improved algorithms aimed at predicting possible cross-feeding interactions between species; in particular, we can predict, using flux balance models, what metabolites can be donated by a given species in a metabolically costless way, i.e. without affecting its growth capacity; we expect that this algorithm will lead to large-scale predictions of possible metabolic interactions between different species or cell types. A specific question we have been focusing on is how these interactions depend on the availability of molecular oxygen in the environment, in an attempt to understand how the rise of oxygen in the atmosphere may have favored the emergence of multicellular organization. (ii) the development on a platform for the simulation of spatio-temporal dynamics of metabolism in multi-species ecosystems. This platform, a hybrid between a dynamic-flux balance simulation and a finite differences approximation of reaction-diffusion, will be freely available and open source. We expect that this tool (for which we have a working prototype) will have broad applicability in helping understand the evolution and dynamics of metabolism, and the interplay between environmental chemistry and biochemical networks.
Genetic interactions and modularity in biological systems
Epistasis constitutes a measure of how genes interact with each other in determining phenotypes. We and others have recently shown how epistasis can be used as an organizing principle for biology, providing insight into the modular architecture of genetic networks, and helping understand global constraints on evolutionary adaptation. We are interested in understanding whether epistasis can be inferred based on the mathematical dependence of a global system-level trait (e.g. fitness) on lower-level traits (e.g. specific molecular or cellular properties). Among other results, we recently derived a general expression for epistasis given an arbitrary functional dependence of a high-level trait on lower-level traits. This expression demonstrates that non-zero epistasis relative to fitness can emerge despite absence of epistasis relative to lower-level traits, leading to a new formal characterization of the concept of independence between biological processes. This approach is extremely general, and it can be extended to describe interactions from the level of single genes up to whole organisms and multicellular systems.
PROJECT INVESTIGATORS:Daniel Segre
PROJECT MEMBERS:Hsuan-Chao Chiu
RELATED OBJECTIVES:Objective 4.1
Earth's early biosphere.
Production of complex life.
Environment-dependent, molecular evolution in microorganisms
Co-evolution of microbial communities
Effects of environmental changes on microbial ecosystems