TMS Home Page Presenting a Web-Enhanced
Article from JOM

View Current Issue




Materials Informatics 2008: Overview Vol. 60, No.3 p. 53-55

Learning from Systems Biology: An “Omics”
Approach to Materials Design

Krishna Rajan

About this Issue


JOM in Print
The print and/or PDF versions of the article can be acquired.


Figure 1
The logic of information flow and knowledge discovery in classical research methodology. The example provided here addresses the use of qualitative reasoning to simulate and identify metabolic pathways.5


Figure 2
A comparison of length and time scales in the life sciences, showing remarkable similarity to those observed in the materials sciences. (Taken from Reference 2.)


Figure 3
An example of a “regulatory network” linking diverse sets of information from both theory and experiment in the study of materials degradation due to irradiation.4








Questions? Contact
2008 The Minerals, Metals & Materials Society

An understanding of systems biology provides an excellent paradigm for the materials scientist. Ultimately one would like to take an “atoms-applications” approach to materials design. This paper describes how the concepts of genomics, proteomics, and other biological behavior which form the foundations of modern biology can be applied to materials design through materials informatics.


…describe the overall significance of this paper?
This paper describes how the concepts of systems biology are applicable to the field of materials science and engineering.

…describe this work to a materials science and engineering professional with no experience in your technical specialty?
This paper describes how informatics is based on the use of mathematics and modeling strategies for linking length scales in materials behavior.

…describe this work to a layperson?
The paper describes how the concepts of genomics, proteomics, and other biological behavior which form the foundations of modern biology can be applied to the design of materials from the “atom to applications.”

The concept of complexity in biology and how to assess the links between information at the molecular level to that at the living organism (e.g., genomics, proteomics, etc.) is the foundation of systems biology. The understanding of systems biology provides an excellent paradigm for the materials scientist. Ultimately one would like to take an “atoms-applications” approach to materials design. How do we organize atoms and build systematically structural units at increasing length scales to the final engineering component or structure? At present we need to rely on extensive prior knowledge with experiments, computation, and even failure analysis to understand the complex network of interactions of materials behavior which govern the performance of an engineering system. The problem is that even with advanced experimental and computational tools, the rate of discovery is still slow, only punctuated by unexpected findings (e.g., superconducting ceramics, conducting polymers) which stimulate new areas of research and development. The iterative approach as shown in this paper is common to many fields as one tries to link observations with models. The challenge is to develop models that capture the system behavior by accounting for all the different levels of information that contribute to the systems behavior.

The goal of modern systems biology is to understand physiology and disease from the level of molecular pathways, regulatory networks, cells, tissues, organs, and ultimately the whole organism.2 As currently employed, the term “systems biology” encompasses many different approaches and models for probing and understanding biological complexity, and studies of many organisms from bacteria to humans. A similar paradigm exists for materials (e.g., atoms to airplanes).2,3

As aptly described by E.C. Butcher et al.,2 the “-omics” (bottom-up) approach focuses on the identifi cation and global measurement of molecular components. Modeling (the top-down approach) attempts to form integrative (across scales) models of human physiology and disease, although with current technologies, such modeling focuses on relatively specific questions at particular scales (e.g., at the pathway or organ levels). An intermediate approach, with the potential to bridge the two, is to generate profiling data from high-throughput experiments designed to incorporate biological complexity at multiple levels: multiple interacting active pathways, multiple intercommunicating cell types, and multiple environments.

A similar challenge occurs in materials science, identifying pathways of how chemistry, crystal structure, microstructure, processing variables, and component design and manufacturing “communicate” with each other to define performance. This forms the materials science equivalent of the biological regulatory network.


Because biological complexity is an exponential function of the number of system components and the interactions between them, and escalates at each additional level of organization (Figure 1), such efforts are currently limited to simple organisms or to specific minimal pathways (and generally in very specific cell and environmental contexts) in higher organisms. The same can be said of complexity in materials science. Even if our ability to measure molecules and their functional states and interactions were adequate to the task, computational limitations alone would prohibit the understanding of cell and tissue behavior from the molecular level. Thus, methodologies that filter information for relevance, such as biological context and experimental knowledge of cellular and higher level system responses, will be critical for successful understanding of different levels of organization in systems biology research. As described by H. Kitano,5 a cycle of biological research begins with the selection of contradictory issues of biological signifi cance and the creation of a model representing the phenomenon. Models can be created either automatically or manually. The model represents a computable set of assumptions and hypotheses that need to be tested or supported experimentally.

A similar analogy may be applied to materials science in trying to explain an unexpected or unusual materials behavior such as the discovery nearly two decades ago of high-temperature superconductivity being exhibited by oxide systems. Up to that point the majority (but not all) of research in this field was focused on intermetallics. The new discovery at the time by Bednorz and Mueller in 1986 spawned a vast array of studies both experimental and theoretical to gain a better understanding of the causes of this important materials behavior. This, of course, was part of a cycle of hypothesis-driven research in superconductivity that has had a long and distinguished history.

The computational simulations (biologists refer to them as “dry” experiments) on models reveal computational adequacy of the assumptions and hypotheses embedded in each model. Inadequate models would expose inconsistencies with established experimental facts, and thus need to be rejected or modified. Models that pass this test become subjects of a thorough system analysis where a number of predictions may be made. A set of predictions that can distinguish a correct model among competing models is selected for experimental validation (called “wet” experiments by the biologists). “Successful” experiments are those that eliminate inadequate models.

Models that survive this cycle are deemed to be consistent with existing experimental evidence. While this is an idealized process of systems biology research, the hope is that advancement of research in computational science, analytical methods, technologies for measurements, and genomics/material informatics can transform research to fit this cycle for a more systematic and hypothesis-driven science.


As suggested by T. Ideker and D. Lauffenberger,6 relationships between different components of information can be extracted from the scaffold using high-level computational models, which identify the key components, interactions, and infl uences required for more detailed low-level models. Large-scale experimental measurements validate high-level models, whereas targeted experimental manipulations and measurements test low-level models. The ultimate goal of knowledge discovery is achieved in systematic integration of data, correlation analysis developed through data mining tools, and most importantly, validated by fundamental theory and experiment based science of materials.

The sources of data can be varied and numerous, including computer simulations, high-throughput experimentation via combinatorial experiments and large-scale databases of legacy information. The application of advanced data mining tools permits the processing of very large sets of information in a robust yet rapid manner. The collective integration of statistical learning tools (the high-level models as shown above) with experimental and computational materials science allows for an informatics driven strategy for materials design.7–10

Ultimately the processing-structure-properties paradigm which forms the core of materials development is based on understanding multivariate correlations and their interpretation in terms of the fundamental physics, chemistry, and engineering of materials. The field of materials informatics can advance that paradigm in a significant manner. A few critical questions may be helpful to keep in mind in building the informatics infrastructure for materials science.

How can data mining/machine learning best be used to discover what attributes (or combination of attributes) in a material may govern specific properties? Using information from different databases, we can compare and search for associations and patterns that can lead to ways of relating information among these different datasets.

What are the most interesting patterns that can be extracted from the existing material science data? Such a pattern search process can potentially yield associations between seemingly disparate data sets as well as establish possible correlations between parameters that are not easily studied experimentally in a coupled manner.

How can we use mined associations from large volumes of data to guide future experiments and simulations? How does one select from a materials library, and which compounds are most likely to have the desired properties? Data mining methods should be incorporated as part of design and testing methodologies to increase the efficiency of the material application process. For instance, a possible test bed for materials discovery can involve the use of massive databases on crystal structure, electronic structure, and thermochemistry. Each of these databases by themselves can provide information on over hundreds of binary, ternary, and multicomponent systems. Coupled to electronic structure and thermochemical calculations one can enlarge this library to permit a wide array of simulations for thousands of combinations of materials chemistries. Such a massively parallel approach in generating new virtual data would be daunting if not impossible were it not for data mining tools as proposed here.


The linking of length scales is a pervasive theme in both biology and materials science. Understanding the complexity across these length scales requires a systems approach to both disciplines. The gene-to-organ or atomto- application paradigm for design can serve as the “omics” approach for materials science and informatics is the enabling toolkit for this to occur.


K. Rajan acknowledges support from the Office of Naval Research for Multidisciplinary University Research Initiative program: Novel Vaccines: Targeting and Exploiting the Bacterial Quorum Sensing Pathway, Award No. N00014-06-1-1176, and the National Science Foundation International Materials Institute Program for Combinatorial Sciences and Materials Informatics Collaboratory, Grant no. DMR- 0603644.


1. R.D. King et al., Bioinformatics, 21 (2005), pp. 2017–2026.
2. E.C. Butcher et al., Nature Biotechnology, 22 (2004), pp. 1253–1259.
3. A.K. Noor et al., Computers and Structures, 74 (2000), pp. 507–519.
4. B.D. Wirth et al., Nuclear Instruments and Methods in Physics Research B, 180 (2001), pp. 23–31.
5. H. Kitano, Science, 295 (2002), pp. 1662–1664.
6. T. Ideker and D. Lauffenberger, Trends in Biotechnology, 21 (2003), pp. 255–262.
7. C. Suh et al., “Informatics Methods for Combinatorial Materials Science,” Combinatorial Materials Science, ed. B. Narasimhan, S.K. Mallapragada, and M.D. Porter (Hoboken, NJ: John Wiley, 2007), chapter 5.
8. C. Suh and K. Rajan, QSAR and Combinatorial Sciences, 24 (2005), p. 114.
9. Zi-Kui Liu et al., JOM, 58 (11) (2006), pp. 42–50.
10. K. Rajan, Materials Today 8 (2005), pp. 38–45.

Krishna Rajan is with the Department of Materials Science and Engineering, NSF Combinatorial Sciences and Materials Informatics Collaboratory— International Materials Institute (CoSMIC-IMI), Iowa State University, Ames 50011. Dr. Rajan can be reached at