Biological robustness

Key Points

  • Robustness is a ubiquitous feature of biological systems. It ensures that specific functions of the system are maintained despite external and internal perturbations. System control, alternative (or fail-safe) mechanisms, modularity and decoupling are the underlying mechanisms that produce robustness.

  • Robustness facilitates the evolvability of complex dynamic systems. Evolution, given enough time, might select a robust trait that is tolerant against environmental perturbations. This interlinks the properties of robustness and evolvability. Robustness is ubiquitous in biological systems that have evolved.

  • There are specific architectural requirements for robust and evolvable systems — genetic buffering, robust modules and bow-tie architecture. These architectural requirements are the basis for the system's robustness against environmental perturbations, but congruent with genetic perturbations; they facilitate generation of a flexible phenotype.

  • Systems that are robust involve intrinsic trade-offs. Enhanced robustness against certain perturbations has to be balanced by extreme fragility elsewhere. This robust yet fragile nature, predicted by the highly optimized tolerance (HOT) theory, is a fundamental property of the system that has been optimally designed or has evolved to cope with perturbations. There are also other trade-offs in the system's performance and resource demands.

  • Diseases can be thought of in terms of the exposed fragility of robust yet fragile systems. The design of effective countermeasures requires proper understanding of a system's behavioural and failure patterns. Diabetes mellitus, cancer and HIV infection represent the typical failure of such a system that requires systematic countermeasures to control robustness of an epidemic state. Countermeasures include systematic intervention to control a system's dynamics, attack fragility or introduce decoys to re-establish control.

  • Developing a theory of biological robustness with a solid mathematical foundation that can realistically represent biological systems is a difficult challenge. Research into non-linear dynamics, control theory and non-equilibrium theory is urgently required, but it has to be careful to capture the essential structural complexity and heterogeneity of biological systems.


Robustness is a ubiquitously observed property of biological systems. It is considered to be a fundamental feature of complex evolvable systems. It is attained by several underlying principles that are universal to both biological organisms and sophisticated engineering systems. Robustness facilitates evolvability and robust traits are often selected by evolution. Such a mutually beneficial process is made possible by specific architectural features observed in robust systems. But there are trade-offs between robustness, fragility, performance and resource demands, which explain system behaviour, including the patterns of failure. Insights into inherent properties of robust systems will provide us with a better understanding of complex diseases and a guiding principle for therapy design.

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Figure 1: Robust reactions of the system: to stay or to change.
Figure 2: Explaining robustness — the aeroplane example.
Figure 3: The architectural framework of robust evolvable systems.


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I would like to thank members of the Sony Computer Science laboratories, Inc. and ERATO-SORST Kitano Symbiotic Systems Project for their fruitful discussions, John Doyle and Marie Csete for critical reading of the initial version of this article, a number of colleagues who discussed the article, and anonymous referees for informative comments. This research is, in part, supported by the ERATO-SORST programme (run by the Japan Science and Technology Agency) of the Systems Biology Institute, the Center of Excellence programme, the special coordination funds (Ministry of Education, Culture, Sports, Science, and Technology) to Keio University and the Air Force Office of Scientific Research (AFOSR/AOARD).

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Gene Ontology

Kitano's web page



Part of a bacteriophage life cycle in which its genome is expressed to cause dissolution of the bacterial host cell, leading to manufacture of more bacteriophage particles and subsequent infection of other cells.


Part of a bacteriophage life cycle, during which its genetic material is integrated into the genome of its bacterial host, where it remains in a latent state.


A pathway that regulates the anteroposterior identity of segments during insect development.


A diffusible signal that acts at a distance to regulate pattern formation in a dose-dependent manner.


A point or an orbit in the phase space where different states of the system asymptotically converge.


A multi-dimentional space that represents the dynamics of a system. For a system with N-variables, a phase space is a 2N dimensional space composed of N-variables and their time derivatives.


A method of feedback control in which control is proportional to the integral of the systems' output.


The process of switching from anaerobic to aerobic respiration.


The buffering or stabilization of developmental pathways against mutational or environmental perturbations, by several genetic factors.


A theory proposed by Motoo Kimura which states that most variations at the molecular level are neutral to selection.


A property of a process that refers to the coupling of processes; in this case, a process depends minimally on other components or processes; example include neural relays or signal transduction pathways, in which individual components often have a switch-like capacity to exist in active or inactive states.


Systems that are based on epigenetic variations and selection; such as angiogenesis and nerve outgrowth.


A structure that has various inputs (fan-in) and outputs (fan-out) that are connected by a knot, resembling a bow-tie.


A feature that is characteristic of system-level dynamics that cannot be attributed to any of its components. The existence of an emergent property indicates that the whole is more than just the sum of the parts.


A sub-network in which there are a large number of components that have extensive internal connections.


A theory about the dynamic properties of systems that are designed, or evolved, to be optimal (either towards a global optimum or sub-optimum). The theory predicts whether systems that are robust against certain perturbations are fragile against unexpected perturbations.


A phenomenon whereby certain systems reach a crucial state through their intrinsic dynamics, independently of the value of any control parameters.


The theory about the design of optimal control methods for engineered objects. It is one of the most successful fields in which mathematical principles are directly applied to practical products, such as aeroplanes, hard disks, automobiles, robotics and chemical plants, and enables them to function properly. Usually, the theory is concerned with how feedback control can be used in various cases to attain optimal design behaviour.


A theorem by Claude Shannon which indicates that for a given channel there exists a code that will permit the error-free transmission across the channel at a rate R, provided R≤C, where C is the channel capacity. This means that the probability of error will not equal zero when R>C, that is, transmission is larger than channel capacity.


A thermodynamics principle which states that if a dynamic equilibrium is disturbed by changing the conditions, then the system tends to adjust to a new equilibrium counteracting the change.


This is a chemical reaction that is widely used to demonstrate transition from the near-equilibrium state to the far-from-equilibrium state. When a low level of heat is applied, it is dissipated without affecting the qualitative characteristics of the medium, but when additional heat is applied, the system undergoes a drastic change, and a circulating flow of chemicals emerges.

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Kitano, H. Biological robustness. Nat Rev Genet 5, 826–837 (2004).

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