Exploring design principles of cellular information processing

As a summary, this work attempts to explore and uncovered design principles of certain dynamics of cellular networks by combining evolution in silico with rule-based modelling approach. Biological systems exhibit complex dynamics, due to the complex interactions in the intra- and inter- cellular biochemical reaction networks. For instance, signalling networks are composed of many enzymes and scaffolding proteins which have combinatorial interactions. These complex systems often generate response dynamics that are essential for correct decision-makings in cells. Especially, these complex interactions are results of long term of evolutionary process. With such evolutionary complexity, systems biologists aim to decipher the structure and dynamics of signalling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. In my PhD study, with collaborators I construct the BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for evolution of cellular networks with theoretically unbounded complexity by combining rule-based modelling with an encoding of networks that is akin to a genome. BioJazz can be used to implement biologically realistic selective pressures, and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. It is provided as an open-source tool to facilitate its further development and use. I use this tool to explore the possible biochemical designs for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key biochemical mechanism for both dynamics. Detailed analysis of these evolved networks revealed that enzyme sequestration enables both ultrasensitive and adaptive response dynamics. I verified this proposition by designing a generic model of a signalling cycle, featuring two enzymes and a sequestering (scaffold) protein. This simple system is capable of displaying both ultrasensitive and adaptive response dynamics, even more interestingly modulating the system switching between two response dynamics through perturbing the scaffold protein. These results show that enzyme sequestration can be exploited by evolution so to generate diverse response dynamics in signalling networks. From evolutionary simulations towards ultrasensitivity, bistable dynamics emerged as an alternative solution. On one hand, inspired by such results I used the fitness function as an objective function combined with different constraints to design and optimise bistable signalling networks with completely new structure and mechanism. Studying designed bistable signalling network explicates how such bistable network can be experimentally implemented. On the other hand, from studying the evolved bistable networks allosteric enzymes catalysing futile cycles appear to be a new mechanism of bistability in signalling networks. Furthermore, one of the smallest bistable signalling motifs is derived. This motif is composed of one kinase protein with two distinct conformational states and one substrate subject to phosphorylation by the kinase and auto-dephosphorylation reactions. The sufficient and necessary condition on parameters, with which the signalling motif displays bistable response dynamics, is analytically defined. By expanding the systems with more kinases, unlimited multistability emerges with potentials of implementing complex logic gates and cell state transitions. Further exploring the discovered and natural signalling networks implies shared design patterns. Motivated by searching structural boundaries between monostationary and multistationary networks, I performed algorithmic searching of multistationary signalling networks intending to find the sufficient structural conditions for multistationarity in signalling networks.