Interdisciplinary Relationships Between Biological and Physical Sciences
IInterdisciplinary Relationships Between Biological and PhysicalSciences
Paulo E. P. Burke and Luciano da F. Costa Bioinformatics Graduate Program, University of São Paulo, São Carlos, SP, Brazil São Carlos Institute of Physics, University of São Paulo, PO Box 369, São Carlos, 13560-970, SP, Brazil
Abstract
Several interdisciplinary areas have appeared at theinterface between biological and physical sciences.In this work, we suggest a complex network-basedmethodology for analyzing the interrelationships be-tween some of these interdisciplinary areas, includingBioinformatics, Computational Biology, Biochem-istry, among others. This approach has been appliedover respective data derived from Wikipedia. Re-lated reviews from the scientific literature are alsoconsidered as a reference, yielding a respective bipar-tite hypergraph which can be used to gain insightsabout the interrelationships underlying the consid-ered interdisciplinary areas. Several interesting re-sults are obtained, including greater interconnectionbetween the considered interdisciplinary areas withbiological than with physical sciences. A good agree-ment was also found between the network obtainedfrom Wikipedia and the interrelationships revealedby the literature reviews. At the same time, the for-mer network was found to exhibit more intricate re-lationships than in the hypergraph derived from theliterature review.
Scientific research has become increasingly interdisci-plinary [1, 2]. It also has been suggested that inter-disciplinary research tends to be intensely cited [3].Among all combinations of disciplines, the relation-ship between biological and physical sciences holds particular interest because of its potential impact onsociety.It is particularly challenging to trace back the ori-gin of the interplay between biological and physicalknowledge, perhaps as a consequence of the intrinsicrelationship between these two areas, which tend tomake almost any related research multidisciplinary.Nevertheless, we may consider as an important markthe application, in the 19th century, of chemistrytechniques to the study of proteins[4], which had beenjust discovered. The term “Biochemistry” was thencoined in order to name the intersection of chemistryand biology.Another important starting-point that can be high-lighted consists in the integration of mathemat-ics and biology developed by Alfred J. Lotka andVito Volterra’s work on population dynamics in1926, commonly known today as the predator-preymodel [5]. Decades later, the now well-establishedfield of Biochemistry relied on the integration ofconcepts from several other areas. For example,in the early 60s computational approaches poweredthe elucidation of amino-acid sequences and three-dimensional structures of proteins[6]. At that mo-ment, the most common term used to identify theapplication of mathematical and computational tech-niques to biological problems was “Computational Bi-ology”. Though still adopted today, this terminologyhas been almost subdued by the term “Bioinformat-ics”, which was in great part motivated by the adventof DNA sequencing [7] in 1977, and further, to theHuman Genome Project[8] in 1990. Nowadays, some1 a r X i v : . [ c s . D L ] M a y esearches claim it is nearly impossible to achievesubstantial scientific advances in biology withouta strong computational basis[9, 10]. In fact, theoutcomes from such interactions have been centralto answer fundamental questions of biology[11, 12]and to push forward applied fields such as precisionmedicine[13, 14].Given the fast growth and importance of the inter-face between biological and physical sciences throughareas such as Biochemistry, Computational Biology,and Bioinformatics, it becomes important to un-derstand how these areas interrelate one another.Though we could try understanding what is meantby each of these areas simplistically from their names(e.g., biochemistry as corresponding to chemistry inbiology), the fast changes characterizing the devel-opment of modern science, and the appearance ofnew interfaces between branching subareas, makes aprecise definition of the considered multidisciplinaryareas very difficult. Even so, efforts toward under-standing, even in approximate terms, the meaningof existing multidisciplinary areas remain a criticalissue for several reasons, including the need to con-tinuously organizing knowledge and results, properlyidentifying interfaces and sub-areas, more effectivelyindex the related literature, orient programs of study,among many other possibilities.Many are the works which aim at understand-ing how scientists manage interdisciplinarity [15] andwhat is its impact on science [16]. However, we lackapproaches that can treat the organization of the in-teraction between different areas of knowledge perse . Fortunately, due to the development of new in-formation science concepts and methods, such as incomplex networks [17] and scientometry [18, 19, 20],it becomes possible to develop quantitative methodscapable of revealing some of the main aspects of sev-eral inter and multidisciplinary areas through data.In this work, we propose a network approach to esti-mate knowledge interaction between scientific areasbased on a given literature database. More specif-ically, we create a citation network based on arti-cles related to areas of interest from a given databaseand their respective references. Then, we generate ahigher-level network which captures the relationshipsbetween these areas based on the former citation net- work. As a case example, we use Wikipedia articlesrelative to biological and physical topics, as well asinterdisciplinary areas which derive from them, in or-der to understand how the knowledge of these areasinteract one another. We also perform a traditionalliterature review of the mentioned topics in order tohave a reference for comparison.This paper is organized as follows. We first de-scribe the general idea of constructing article net-works and how we derive knowledge networks fromit. Then, we present the Wikipedia database whichis used to perform our analysis. The outcomes of ap-plying our methodology to the Wikipedia databaseare then analyzed and discussed. At last, we presentsome conclusions about this work and possible futureapplications. Knowledge is mainly perpetuated through its registeron some media that can be propagated and referred.Until now, the most efficient method found to storeknowledge is to write it down in the form of books orarticles, in paper or digitally. Also, it is very likelythat most of the registered knowledge derives from,or relates to, some previous knowledge or informa-tion. In scientific literature, as well as in other areas,the relationship between pieces of literature are reg-istered in the form of references between texts. Thus,the web formed by texts and references between themcan unveil a higher-level structure of knowledge.In order to assess this web structure of knowledge,let us consider a collection of texts associated witha given subject. These texts, additionally to theircontents, have references to other related texts, be-longing or not to this collection. With articles andreferences, we can then construct an article network,following the procedure detailed in Section 2.1. Then,we can estimate the knowledge interactions betweenthose subjects by grouping their respective articles.To avoid some unwanted biases, we normalize andgroup the nodes following the procedure described inSection 2.2.2igure 1: Illustration of the process of selecting articles, creating a citation network between them, andabstracting a respective knowledge network.
Networks have been used to model and analyze sev-eral kinds of data, from social interaction to trans-portation. Essentially, networks are composed of twoelements: vertices and edges. Real-world data involv-ing discrete objects can be mapped into networks byassigning entities to vertices and using links to rep-resent relationships between the entities. In the caseof scientific literature, we can map texts into verticesand respective hyperlinks or bibliographic referencesas edges. In mathematical terms, our network cantherefore be described as G = ( v, e ) , where N is thetotal of texts, v = { v , v , . . . v N } are the vertices,and e = { ( v i , v j ) , ( v k , v l ) , . . . ( v m , v n ) } are edges indi-cating respective relationships between the texts. The number of articles contained in each of the cat-egories of interest can substantially vary in magni-tude. This variance can be a consequence of severalfactors, including area specificity, existence time, andfunding, among others. This variation implies severaldifficulties while analyzing relationships between theconsidered areas. For instance, the links between twolarger areas would tend to appear in a more signifi-cant number than between two areas with a relativelysmaller number of works. Thus, if one is interestedin quantitatively comparing the relationship between any two different areas, it is necessary to normalizeconcerning the size of the areas. In order to min-imize this potential bias, we sampled the networkwhile selecting a fixed number of vertices for everyone of its areas, as exemplified in Figure 2. Observethat the number of selected vertices must be smallerthan the size of the category containing the small-est number of articles. Next, the articles so selectedwithin each area were collapsed into a single vertex,resulting in a subsumed network where the numberof vertices is equal to the number of areas. The fre-quency of connections between two areas, i.e. the ex-ternal edges , is now represented as the weight of theedge which connects their respective vertices, whilethe frequency of connections within articles of thesame area, the internal edges , is stored as a nodeweight. After sampling the network several times,an average of the obtained networks will result inthe normalized network of knowledge. Finally, theweight of each edge/node was divided by the sum ofthe weights of all edges/nodes in order to the resultbecome invariant to the number of selected nodes perarea.
Wikipedia is currently the most extensive collabo-rative encyclopedia. Nowadays, the English versioncontains around 5.6 million articles with subjectsranging from science to popular culture, history, art,3igure 2: Illustration of the network sampling pro-cedure. In this example, four nodes from each areaare randomly selected while the links between themare kept. The resulting collapsed network has itslinks weighted by the total connections between areasyielded by the original nodes.and others.We selected a subset of articles from Wikipedia where their content refers to biological and physicalsciences topics. More specifically, we selected arti-cles originally assigned to the following categories:Chemistry, Mathematics, Applied mathematics, Dy-namical Systems, Computer Science, Statistics, En-gineering, Biomedical Engineering, Biology, Ecol-ogy, Medicine, Health Sciences, Molecular Biology,Bioinformatics, Biochemistry, Computational Ecol-ogy, Biotechnology, Systems Biology, and Computa-tional Biology. Observe that these categories cor-respond to respective tags available directly fromWikipedia. Those categories were chosen in orderto cover the main areas within biological and physi-cal sciences as well as interdisciplinary areas, namelyBioinformatics, Computational Biology, BiomedicalEngineering, Biochemistry, Computational Ecologyand Systems Biology, which are intrinsically relatedto those two major areas. Given that Wikipedia’scategories are hierarchically organized, we also se-lected subtopics at one hierarchical level downwardsfrom the categories mentioned above. The total num-ber of articles available in each category, considering The English version of the Wikipedia database can befreely downloaded from dumps.wikimedia.org/enwiki . their respective subtopics, is depicted in Fig. 3.Figure 3: Number of articles in each of the consideredcategories. Relationships between scientific areas can be esti-mated in several ways. Here, we used Wikipedia’sarticles as a means of quantitatively measuring theinteractions between biological and physical areas, aswell as interdisciplinary fields which might be a con-sequence of the interplay between these two majorareas. In order to do so, we obtained articles fromWikipedia’s database which were already assigned tolabels referring to the scientific areas listed in Section2.3, as well as their respective sub-categories withinone level in depth. However, some categories, namelyBiomedical Engineering, Systems Biology, Statistics,Computational Biology, and Computational Ecology,were excluded from the analysis due to their verysmall number of articles when compared to the oth-ers categories, as observed in Figure 3.We constructed the article network which con-4ained 19,400 Wikipedia articles connected by131,657 hyperlinks. To generate the knowledge net-work, we normalized the connections between areasfollowing the procedure described in Section 2.2 bysampling 200 nodes of each area, and taking an av-erage over 10,000 samples. We also sampled the net-work using 100 and 300 nodes per category, and nosignificant difference in the results was noticed (datanot shown). The obtained knowledge network, de-picted in Figure 4a, is fully connected. However, itsstrongest edges (highlighted in orange) show a well-bounded community structure forming two groups.The group on the left is composed solely of physi-cal sciences, namely Mathematics, Applied Mathe-matics, Dynamical Systems, Computer Science andEngineering.On the other hand, the group on the right mixesall biological sciences, interdisciplinary areas, andChemistry. This emergence of two groups indicates asubstantial level of separation between physical andbiological sciences. Interestingly, despite all interdis-ciplinary areas being strongly connected to biologicalsciences, they display different connecting patterns.In Figure 4b we can observe that Biochemistry hasmost of its connections pointing to Chemistry, Molec-ular Biology, Ecology, and Biotechnology. However,even Bioinformatics also being strongly connected tothe second group, in Figure 4b we can observe thatit has more connections with the physical group thanthe former area, being especially connected with Ap-plied Mathematics and Computer Science.Regarding the balance between internal and ex-ternal edges of each area, all fields have more in-ternal than external edges. However, a trend canbe observed in Figure 4c where some areas have ahigher percentage of external links than others. Thefirst four areas with a lower percentage of externaledges are all from physical sciences. Contrariwise,the last six areas which yield the highest externaledges percentages comprise almost all biological ar-eas plus Chemistry and Applied Mathematics. Allinterdisciplinary areas plus Health Sciences presenteda midterm percentage of external edges in compari-son to the others. This analysis suggests a more inte-grated environment within the biological than amongphysical sciences.
In order to provide a comparative basis to our study,we gathered information about the disciplines thatarose from the physical-biological interchange by per-forming a systematic literature review, looking forcomprehensive texts which provide broad overviewsor historical perspectives of their respective areas.We performed searches in Google Scholar using askeywords the areas’ names combined with the words“review”, “overview” and “history”. Texts referringonly to specific topics from the areas were not con-sidered. The selected manuscripts were then orga-nized in Table 1 by row according to its interdisci-plinary area and their reference in each column indi-cates that the article cites a relationship between theinterdisciplinary area and the corresponding physi-cal/biological field.Regarding the considered biological and physicalareas, we have from Table 1, Computer Scienceshowed to be highly referred among almost all inter-disciplinary sciences. It is intuitive to think that thevast majority of analyses that are carried out nowa-days in any area make use of computational resources.On the biological side, Molecular Biology has a pre-dominance in almost all interdisciplinary areas exceptComputational Ecology and Biomedical Engineering.With respect to the interdisciplinary areas, al-though we can say that Bioinformatics is today themost known name among all considered interdisci-plinary areas, few were the scientific manuscriptsfound that discuss the area as a whole rather thansome specific topic. Nevertheless, the so foundmanuscripts suggest an almost exclusive relationshipbetween Computer Science and Molecular Biology.A similar scenario can be observed when consideringSystems Biology. Despite being a younger scientificfield, it underwent fast development at the beginningof this millennium due to its power to integrate bio-logical data, and therefore, provide more faithful andlarger computational models of biological systems.The data in Table 1 is represented as a diagram inFigure 5. It is easy to observe that Computational Bi-ology extends through a wider range of areas, perhapsas a consequence of existing for a longer time. How-5 a) (b) (c)
Figure 4: (a) Scientific areas network visualization. Biological, Physical, and Interdisciplinary areas arerepresented by green, blue, and yellow vertices, respectively. The size of each vertex is proportional to thenumber of connections within articles of the same area. The thickness of the edges is proportional to thenumber of links between the two areas it connects. Highlighted connections between areas compose thesmallest set of strongest edges which keep all nodes connected into a single component. (b) Percentage ofexternal connections of each interdisciplinary area with the other biological and physical areas. (c) Theproportion of the internal and external edges of each area.ever, despite its historical importance, Biochemistry,our results suggest that it remained mostly within itsoriginal realm.If we compare the results obtained from Wikipediawith those derived from the performed literature re-view, some differences become evident. First of all,four out of all six considered interdisciplinary areascould not be considered in the Wikipedia analysis be-cause of their extremely low number of articles. Thepossible reasons for this relatively small number ofinclude area specificity, existence time, and funding,among others. On the other hand, for the remain-ing areas with many related articles in the Wikipediaanalysis, namely Bioinformatics and Biochemistry,we could not find more than three and two respec-tive review articles.It is worth noticing that the subject of Biotech-nology, considered as a biological area, could not befound in any of the selected review articles. Never-theless, we have from Figure 4a that Biotechnologyhas strong interactions with both Bioinformatics andBiochemistry areas.
As implied by its own name, interdisciplinary sci-ences emerge from interplays between different disci-plines, and their importance to scientific advance hasgrown steadily. Among all possible combinations be-tween knowledge fields, the interaction between phys-ical and biological sciences is one that deserves par-ticular attention. The outcomes from this interactionare already affecting society as a whole, once theypermeate vital sectors such as health and food pro-duction. Thus, it is essential to understand how thecombinations of physical and biological sciences areestablishing, evolving and influencing one another.To do so, we proposed a data-driven methodologywhere relationships between scientific areas can beestimated from the scientific literature. We used theWikipedia’s database to obtain articles whose cate-gories were related to several scientific areas underbiological and physical sciences, as well as interdisci-plinary fields that have emerged from them. Giventhat each of the considered areas had a substantially6able 1: Literature review of interdisciplinary areas
Physical Sciences Biological SciencesMathematics ComputerScience AppliedMathematics DynamicalSystems Chemistry Engineering Ecology MolecularBiology Biology Biotechnology Medicine HealthSciences TotalComputationalEcology [21, 22, 23] [21, 22, 23] [21, 22, 23] [21, 22, 23] 3
Bioinformatics [24, 25, 26] [25] [25, 26] [24] 3
SystemsBiology [27, 28, 29, 30, 31] [28, 29, 31] [27, 28, 29, 30, 31] 5
ComputationalBiology [32, 10] [33, 32, 10] [33] [33, 34] [33, 32, 34] [10, 34] 4
BiomedicalEngineering [35, 36] [35] [37, 35, 36] [37, 35, 36] [37, 35, 36] 3
Biochemistry [38, 39] [39] [38, 39] 2
Total
Figure 5: Diagram of relationships between biologicaland physical sciences derived from the data in Table1.different number of articles, it was necessary to de-vise means for respective normalization, which wasobtained by performing a sampling of the originaldata. We also performed a literature review on theconsidered interdisciplinary topics in order to have areference representing a more traditional approach.Several interesting results have been obtained. Forinstance, we observed a possible greater approxima- tion, in the sense of being more intensely intercon-nected, between the interdisciplinary areas with thebiological instead of with the physical areas. A pos-sible explanation for this interesting structure couldbe that most of the interdisciplinary areas were origi-nally motivated from biology, reflecting an increasingneed for incorporating more and more concepts andtools from the physical sciences In other words, theconsidered interdisciplinary areas would be mostlydriven to biological applications. Other interest-ing results include the centrality of Biochemistryamong the biological areas, as well as the particu-larly strong interconnection between the consideredbiological and physical realms implemented by Chem-istry.Regarding the investigation of review in the relatedscientific literature, we have that Computer Scienceand Molecular Biology are found in most of the re-views. At the same, few reviews covered to a sub-stantial extent the area of Biochemistry. The tablesummarizing this literature review was then used toderive a bipartite hypergraph representation in whicheach interdisciplinary area is represented as respec-tive hyperlink encompassing one or more areas frombiological and physical sciences. The so obtained hy-pergraph provides a putative and approximate char-acterization of the considered interdisciplinary areas.At the same time, it is interesting to observe that, un-like this hypergraph, the network obtained from theWikipedia data is completely interconnected. Thissuggests that the interrelationships between the con-sidered interdisciplinary areas could be more intri-7ate and integrated than that highlighted by respec-tive reviews. Still, a good agreement can be observedbetween these two structures, with exception of theBiotechnology area, which appears well connectedwith other areas in the obtained network derived fromWikipedia while it was not highlighted in any of theconsidered reviews.All in all, we have approached the problem of char-acterizing some of the main interdisciplinary areasbetween the biological and physical sciences in termsof both real data obtained from the Wikipedia, aswell as through the consideration of some respectiveliterature reviews. Though a putative network of in-terrelationships has been obtained, it is importantto bear in mind that these results are, as yet, sub-stantially incomplete and preliminary. The futureavailability of more data and literature reviews canprovide a means for obtaining more complete and ac-curate characteristics by using the same, or similar,methodology. Another interesting prospect for futureresearch regards deriving descriptions of how the in-terdisciplinary areas changed with time.
Paulo E. P. Burke thanks Filipi Nascimento Silva forproviding the filtered Wikipedia data. Luciano da F.Costa thanks CNPq (grant no. 307085/2018-0) forsponsorship. This work has benefited from FAPESPgrant 15/22308-2. This study was financed in partby the Coordenação de Aperfeiçoamento de Pessoalde Nível Superior - Brasil (CAPES) - Finance Code001.
References [1] A. L. Porter and I. Rafols, “Is science becom-ing more interdisciplinary? Measuring and map-ping six research fields over time,”
Scientomet-rics , vol. 81, no. 3, pp. 719–745, 2009.[2] R. Van Noorden, “Interdisciplinary researchby the numbers,”
Nature , vol. 525, no. 7569,pp. 306–307, 2015. [3] S. Chen, C. Arsenault, and V. Larivière, “Aretop-cited papers more interdisciplinary?,”
Jour-nal of Informetrics , vol. 9, no. 4, pp. 1034–1046,2015.[4] G. K. Hunter,
Vital forces : the discovery of themolecular basis of life . Academic Press, 2000.[5] A. A. Berryman, “The Orgins and Evolutionof Predator-Prey Theory,”
Ecology , vol. 73,pp. 1530–1535, oct 1992.[6] J. B. Hagen, “The origins of bioinformatics.,”
Nature Reviews Genetics , vol. 1, no. 3, pp. 231–236, 2000.[7] F. Sanger, S. Nicklen, and A. R. Coulson, “DNAsequencing with chain-terminating inhibitors,”
Proceedings of the National Academy of Sci-ences , vol. 74, pp. 5463–5467, dec 1977.[8] J. Watson, “The human genome project: past,present, and future,”
Science , vol. 248, no. 4951,pp. 44–49, 1990.[9] G. W. Brodland, “How computational modelscan help unlock biological systems,”
Seminarsin Cell & Developmental Biology , vol. 47-48,pp. 62–73, dec 2015.[10] F. Markowetz, “All biology is computational bi-ology,”
PLOS Biology , vol. 15, p. e2002050, mar2017.[11] J. D. J. Han, Y. Liu, H. Xue, K. Xia, H. Yu,S. Zhu, Z. Chen, W. Zhang, Z. Huang, C. Jin,B. Xian, J. Li, L. Hou, Y. Han, C. Niu, andT. C. Alcon, “Developmental systems biologyflourishing on new technologies,”
Journal of Ge-netics and Genomics , vol. 35, no. 10, pp. 577–584, 2008.[12] P. Nurse and J. Hayles, “The cell in an era ofsystems biology,” 2011.[13] S. J. Aronson and H. L. Rehm, “Building thefoundation for genomics in precision medicine,”2015.814] C. Krittanawong, H. J. Zhang, Z. Wang, M. Ay-dar, and T. Kitai, “Artificial Intelligence in Pre-cision Cardiovascular Medicine,” 2017.[15] C. L. Palmer, “Structures and strategies of in-terdisciplinary science,”
Journal of the AmericanSociety for Information Science , vol. 50, no. 3,pp. 242–253, 2002.[16] E. Omodei, M. De Domenico, and A. Arenas,“Evaluating the impact of interdisciplinary re-search: A multilayer network approach,”
Net-work Science , vol. 5, no. 2, pp. 235–246, 2017.[17] F. N. Silva, D. R. Amancio, M. Bardosova,L. d. F. Costa, and O. N. Oliveira, “Using net-work science and text analytics to produce sur-veys in a scientific topic,”
Journal of Informet-rics , vol. 10, no. 2, pp. 487–502, 2016.[18] D. R. Amancio, M. G. Nunes, O. N. Oliveira,and L. F. da Costa, “Using complex networksconcepts to assess approaches for citations in sci-entific papers,”
Scientometrics , vol. 91, no. 3,pp. 827–842, 2012.[19] F. N. Silva, F. A. Rodrigues, O. N. Oliveira, andL. Da, “Quantifying the interdisciplinarity of sci-entific journals and fields,”
Journal of Informet-rics , vol. 7, no. 2, pp. 469–477, 2013.[20] C. Mund and P. Neuhäusler, “Towards an early-stage identification of emerging topics in science-The usability of bibliometric characteristics,”
Journal of Informetrics , vol. 9, no. 4, pp. 1018–1033, 2015.[21] J. Helly, T. Case, F. Davis, S. Levin, andW. Michener, “The State of ComputationalEcology,”
San Diego Super Computer Center ,1995.[22] M. Pascual, “Computational Ecology: From theComplex to the Simple and Back,”
PLoS Com-putational Biology , vol. 1, no. 2, p. e18, 2005.[23] S. Petrovskii and N. Petrovskaya, “Computa-tional ecology as an emerging science.,”
Interfacefocus , vol. 2, pp. 241–54, apr 2012. [24] J. Cohen, “Bioinformatics—an introduction forcomputer scientists,”
ACM Computing Surveys ,vol. 36, pp. 122–158, jun 2004.[25] N. M. Luscombe, D. Greenbaum, and M. Ger-stein, “What is bioinformatics? An introduc-tion and overview,”
Methods of Information inMedicine , pp. 346–358, 2001.[26] H. Stevens,
Life out of sequence : a data-drivenhistory of bioinformatics . The University ofChicago Press, 2013.[27] L. Hood, “Systems biology: integrating technol-ogy, biology, and computation,”
Mechanisms ofAgeing and Development , vol. 124, pp. 9–16, jan2003.[28] H. Kitano, “Computational systems biology,”
Nature , vol. 420, pp. 206–210, nov 2002.[29] H. Kitano, “Systems Biology: A BriefOverview,”
Science , vol. 295, pp. 1662–1664,mar 2002.[30] T. Ideker, T. Galitski, and L. Hood, “A NewApproach to Decoding Life : Systems Biology,”
Annual Review of Genomics and Human Genet-ics , vol. 2, pp. 343–372, sep 2001.[31] P. Kohl, E. J. Crampin, T. A. Quinn, and D. No-ble, “Systems Biology: An Approach,”
ClinicalPharmacology & Therapeutics , vol. 88, pp. 25–33, jul 2010.[32] M. S. Waterman,
Introduction to computationalbiology : maps, sequences, and genomes : inter-disciplinary statistics . Chapman & Hall/CRC,2000.[33] B. M. Slepchenko, J. C. Schaff, J. H. Carson,and L. M. Loew, “Computational Cell Biology:Spatiotemporal Simulation of Cellular Events,”
Annual Review of Biophysics and BiomolecularStructure , vol. 31, pp. 423–441, jan 2002.[34] D. Noble, “The rise of computational biology,”
Nature Reviews Molecular Cell Biology , vol. 3,no. 6, pp. 459–463, 2002.935] J. D. Bronzino,
The biomedical engineeringhandbook . CRC Press, 2000.[36] J. D. J. D. Enderle and J. D. Bronzino,
In-troduction to biomedical engineering . Else-vier/Academic Press, 2012.[37] F. Nebeker, “Golden accomplishments inbiomedical engineering,”
IEEE Engineering inMedicine and Biology Magazine , vol. 21, pp. 17–47, may 2002.[38] P. Singh, H. S. Batra, and M. Naithani, “Historyof biochemistry.,”
Bulletin of the Indian Insti-tute of History of Medicine (Hyderabad) , vol. 34,no. 1, pp. 75–86, 2004.[39] D. Voet and J. G. Voet,