Systems biology

A major challenge for systems biology is to deduce the molecular interactions that underlie correlations observed between concentrations of different intracellular molecules. Although direct explanations such as coupled transcription or direct protein-protein interactions are often considered, potential indirect sources of coupling have received much less attention. In this work we show how correlations can arise generically from a post-translational coupling mechanism involving the processing of multiple protein species by a common enzyme.

Systems as diverse as the world wide web, Internet or the cell are described by highly interconnected networks with amazingly complex structure. Recent studies indicate that the evolution of these complex networks is governed by simple but generic laws, resulting in apparently universal architectural features. I will discuss this amazing order characterizing our interconnected world, and its implications to how we perceive the impact on communications and medicine.

he reverse engineering of biological networks is an important and
interesting problem. Two examples of such networks are gene
regulatory networks, and the relationship of voxels in the brain. We
describe a method for determining possible wiring diagrams for such
networks. The method is based on computational algebra, and a key
part of the method uses computations involving monomial ideals in a
polynomial ring. To illustrate the algorithms, we apply the method
to data coming from fMRI scans of the brain.

Practical applications in modern molecular and systems biology, such as the search for new therapeutic targets for diseases and stem cell reprogramming, have generated a great interest in the cell fate reprogramming, i.e., controlling the internal state of a cell so that it is driven from an initial state to a final target state. Although the topic of controlling the dynamics of a system, of which cell fate reprogramming can be considered a subset, has a long history in control and systems theory, most of this work is not directly applicable to intracellular networks.

Sequence-based computational approaches have revolutionized biological understanding. However, they can fail to explain some biological phenomena. Since proteins aggregate to perform a function instead of acting in isolation, the connectivity of a protein interaction network (PIN) will provide additional insight into the inner working on the cell, over and above sequences of individual proteins. We argue that sequence and network topology give insights into complementary slices of biological information, which sometimes corroborate each other, but sometimes do not.

The field of systems biology has emerged from a confluence of technological advances (DNA sequencing, gene expression profiling, proteomics, metabolomics etc.) and a “systems-level” understanding of biological processes, supported by network theory. Network models are essential in the interpretation of experimental results and, increasingly, in predicting the behaviour of cellular systems subject to experimental perturbations.

Biological networks are dynamic, driven by processes such as development and disease that change the expressed genes and proteins and modify interactions. Understanding how networks remodel could reveal the etiology of developmental disorders and suggest new drug targets for infectious disease. We present new methods that predict how networks change over time through joint analysis of time-domain and static data.

Networks of biological signals guide cells to form
multicellular patterns and structures. Understanding the
design and function of these complex networks is a fundamental
challenge in developmental biology and has clear implications
for biomedical applications, such as tissue engineering and
regenerative medicine. Signaling networks are composed of
highly interconnected pathways involving numerous molecular
components. Precisely to what extent multicellular structures
are susceptible to quantitative variations in underlying