Biological systems attain their functionality through the activity of multiple interacting elements, the distribution of which ranges over many orders of magnitude in both space and time. To a large extent, the means by which the functionality of these networks develops and evolves in relation to the environment is still a mystery.
The Laboratory for Network Biology Research, comprising researchers from four Technion faculties (Medicine, Physics, Electrical Engineering and Chemical Engineering), aims at developing an experimental and theoretical framework for the study of biological networks, with particular emphasis on general mechanisms that allow for robust, yet adaptive, functionality in complex environments.
Our research is centered on two experimental systems originating from different areas of biology, and on a theoretical effort to develop a universal approach to network biology, which, while aiming at principles, is cognizant of the particularities of specific systems.
In the labs of Prof. Marom and Prof. Ziv, large networks of real neurons are studied. When many such neurons are extracted from the brain, placed together and given appropriate nutrients, they extend processes - axons and dendrites - form numerous synaptic connections, and develop complex patterns of activity. The groups of Marom and Ziv analyze the structure and dynamics of these networks and their sensitivities to environmental challenges. Functional capacities such as input classification, adaptation and learning are related to environmental complexity, developmental time course and network structure. Our methodologies allow for simultaneous stimulation and recordings from many individual neurons; continuous, long-term (weeks) concomitant monitoring of neuronal activity, neuronal structure, synaptic size and synaptic distribution; enforcement of developmental constraints at both the structural and activity levels; evaluation of the effects of chemical modulators; and, some control over connections between elements as well as between ensembles. This allows us to record and manipulate network activity, investigate network capacities and follow the underlying structural dynamics over the entire developmental time course of the network.
In the lab of Prof. Braun experiments focus on the evolution of genetic regulatory networks. During natural regulatory evolution, these networks undergo dramatic rewiring events that can increase their complexity and drive organism diversity. We have developed synthetic gene recruitment as a powerful experimental approach, which allows to bring into the lab the processes underlying evolution of genetic networks and to expose the potential of cell evolvability in microorganism populations. A unique experimental setup enables measuring the dynamics of both physiological and gene-expression characteristics of large yeast populations over a wide range of time scales. Our experiments reveal that genetic regulatory networks exhibit high plasticity allowing them to adapt non-specifically to novel challenges. We are currently investigating the molecular mechanisms underlying this plasticity, as well as the role of population structure, phenotypic distributions and dynamics of physiological selection that are involved in the adaptation processes. More generally, the characteristics of evolving genetic regulatory networks draw a fundamental analogy between them and learning neural networks in their ability to create novel behavior in the face of change, while maintaining their functionality. This analogy is fundamental to our theoretical approach to network biology.
We aim to construct a general theoretical framework for network biology, based on the identification of universal vs. system-specific features. Our underlying assumption is that biological systems composed of interacting units, although varying greatly in detail from system to system, nevertheless share some universal characteristics and principles. These include heterogeneity, competition for shared resources, transfer of information among units, adaptive behavior at several levels of organization, a wide range of time scales and the ability to adapt and learn under a variety of unforeseen environmental conditions. Our theoretical team (Brenner, Eldar and Meir) spans a range of fields of expertise and traditions from statistical physics, dynamical systems, signal processing, control theory, information theory and machine learning. While the task at hand is admittedly ambitious, we believe that our combination of several unique experimental systems with a strong and wide-ranging theoretical effort will provide us with an opportunity to advance in this direction.
Faculty affiliated with the Laboratory:
Medicine - Shimon Marom and Noam Ziv
Electrical Engineering - Yonina Eldar and Ron Meir