rithmic discipline not only for systems biology but also for synthetic biology— a new area of biological research that aims at building, or synthesizing, new biological systems and functions by exploiting new insights from science and engineering. An algorithmic approach can help propel this field by providing an in-silico library of biological components that can be used to derive models of large systems; such models could be ready for simulation and analysis just by composing the available modules. ¹⁶

The notion of a library of (biological) components, equipped with attributes governing their interaction capabilities and automatically exploited by the implementation of the language describing systems dynamics, substantially contributes to overcoming the misleading concept of pathways that fills biological papers, where a pathway is posited as an almost-sequential chain of interactions. The theory of concurrency, however, maintains that neglecting the context of interactions (all the other possible routes of the system) produces an incomplete and untrustworthy understanding of the system’s dynamics. Metaphorically, it is not possible to understand the capacity of the traffic organization of a city by looking at single

routes from one point to another, or to fully appreciate a goal in a team sport by looking at the movements of a single player.

At a different level of abstraction, the study of pathways is a reductionist approach that does not take pathway interactions (crosstalk) into account and does not help in unraveling emergent network behavior. The management of hierarchies of interconnected specifications, so typical of computer science, is fundamental for interpreting what systems behavior means, depending on the context and the properties of interest. It could be easy to move to biological networks by considering the biological entities as a collection of interacting processes and by studying the behavior of the network through the conceptual tools of concurrency theory.

Note also that a model repository, representing the dynamics of biological processes in a compact and mechanistic manner, would be extremely valuable in heightening the understanding of biological data and the basic principles governing life. Such a repository would favor predictions, allow for the optimal design of further experiments, and consequently stimulate the movement from data

 

figure 2: the biological systems observed through the window showing the life sciences (green rectangle) can be closely and mechanistically modeled through the use of algorithms (written on the glass of the window) that add causal, spatial, and temporal dimensions to classical biological descriptions. moreover, algorithms can concisely represent the large quantities of data produced by high-throughput experiments (the river of numbers originating from biological elements within the window). equations, currently considered the stars of modeling, are more abstract and hence more distant from living matter. the goal of algorithmic systems biology is to “reach for the moon” through a complete mechanistic model of living systems. (the lighted hemisphere in the picture represents a cell under a digitalization process.)

collection to knowledge production.

Algorithmic systems biology raises novel issues in computing by stepping away from the qualitative descriptions typical of programming languages toward a new quantitative computing. Thus computing can fully become an experimental science, as advocated by Denning, ²⁰ that is suitable to supporting systems biology. Core computing fields would themselves benefit from a quantitative approach; a measure of the level of satisfaction from Web service contracts, for example, or the quality of services in telecommunication networks could enhance our current software-development techniques. Another example is robotics, where a myriad of sensors must be synchronized according to quantitative values. Quantitative computing would also foster the move toward a simulation-based science that is needed to address the increasingly larger dimension and complexity of scientific questions.

It will easily become impossible to have the whole system we design available for testing (examples are the new Boeing and Airbus aircraft) and hence we need to find alternatives for studying and validating the system’s behavior. Simulation of formal specifications is one possibility. Indeed, the programming languages used to model biological systems implement stochastic runtime supports that help in addressing extremely relevant questions in biology such as “How does order emerge from disorder?” The answers could provide

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us with completely new ways of organizing robust and self-adapting networks both natural and technological. Further, the discrete-state nature of algorithmic descriptions makes them suitable for implementing the stochastic simulation algorithm by Gillespie²⁵ or its variants. This approach, originally developed for biochemical simulations, is also suitable for quantitatively simulating systems from other domains; in fact, there are cases in which it can be much faster than classical event-driven simulation. ³⁴

Algorithmic systems biology completely adopts the main assets of our computing discipline: hierarchical, systems, and algorithmic thinking in modeling, programming and innovating. Moreover, because breakthrough results are sometimes the outcome of